diff --git a/.circleci/config.yml b/.circleci/config.yml index 9d9b290652..80651e06ec 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -2,10 +2,17 @@ # # Check https://circleci.com/docs/2.0/language-python/ for more details # +############# +# Automatically generated by ci-generator.py in splunk/security-content +# On Date: 2020-07-09T12:33:27 UTC +# Author: Splunk Security Research +# Contact: research@splunk.com +############# version: 2.1 orbs: aws-cli: circleci/aws-cli@0.1.19 + slack: circleci/slack@3.4.2 dependencies: cache_directories: @@ -38,6 +45,176 @@ executors: working_directory: ~/repo jobs: + test-detections: + executor: content-executor + steps: + - run: + name: checkout repo + command: | + if [[ ! -z "${CIRCLE_PULL_REQUEST}" && ! -z "${CIRCLE_PR_NUMBER}" ]]; then + git clone https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + cd security-content + git fetch origin pull/${CIRCLE_PR_NUMBER}/head:${CIRCLE_BRANCH} + git checkout ${CIRCLE_BRANCH} + elif [ "${CIRCLE_BRANCH}" == "" ]; then + git clone https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + else + git clone --branch ${CIRCLE_BRANCH} https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + fi + - run: + name: checkout attack_range repo + command: | + git clone https://${GITHUB_TOKEN}@github.com/splunk/attack_range.git + - restore_cache: + key: virtualenv + - run: *apt-install + - run: + name: install python dependencies + command: | + cd attack_range + rm -rf venv + virtualenv --python=/usr/bin/python3 --clear venv + source venv/bin/activate + pip install -q -r requirements.txt + - aws-cli/setup: + profile-name: default + - add_ssh_keys: + fingerprints: + - "39:68:78:2b:01:d6:35:eb:78:42:28:58:1d:9c:be:90" + - run: + name: install terraform + command: | + wget https://releases.hashicorp.com/terraform/0.12.24/terraform_0.12.24_linux_amd64.zip + sudo unzip terraform_0.12.24_linux_amd64.zip -d /usr/bin + rm terraform_0.12.24_linux_amd64.zip + terraform --version + - run: + name: detection tests > T1574_009.yml + command: | + cd attack_range + source venv/bin/activate + # change attack_range default ssh keys + sed -i "s/key_name = .*$/key_name = attack-range-ci-job-security-content/g" attack_range.conf + sed -i "s/automated_testing = .*$/automated_testing = 1/g" attack_range.conf + sed -i "s/github_token =.*$/github_token = ${GITHUB_TOKEN}/g" attack_range.conf + sed -i "s/region = us-west-2/region = us-west-2/g" attack_range.conf + sed -i "s/private_key_path =.*$/private_key_path = ~\/.ssh\/id_rsa_3968782b01d635eb784228581d9cbe90/g" attack_range.conf + sed -i "s/windows_domain_controller = .*$/windows_domain_controller = 1/g" attack_range.conf + sed -i "s/capture_attack_data = .*$/capture_attack_data = 1/g" attack_range.conf + cd terraform/ + terraform init + cd .. + python attack_range.py --mode terraform --action test --test_file ../security-content/tests/T1574_009.yml + - run: + name: detection tests > T1021_001.yml + command: | + cd attack_range + source venv/bin/activate + # change attack_range default ssh keys + sed -i "s/key_name = .*$/key_name = attack-range-ci-job-security-content/g" attack_range.conf + sed -i "s/automated_testing = .*$/automated_testing = 1/g" attack_range.conf + sed -i "s/github_token =.*$/github_token = ${GITHUB_TOKEN}/g" attack_range.conf + sed -i "s/region = us-west-2/region = us-west-2/g" attack_range.conf + sed -i "s/private_key_path =.*$/private_key_path = ~\/.ssh\/id_rsa_3968782b01d635eb784228581d9cbe90/g" attack_range.conf + sed -i "s/windows_domain_controller = .*$/windows_domain_controller = 1/g" attack_range.conf + sed -i "s/capture_attack_data = .*$/capture_attack_data = 1/g" attack_range.conf + cd terraform/ + terraform init + cd .. + python attack_range.py --mode terraform --action test --test_file ../security-content/tests/T1021_001.yml + - run: + name: detection tests > T1003_003.yml + command: | + cd attack_range + source venv/bin/activate + # change attack_range default ssh keys + sed -i "s/key_name = .*$/key_name = attack-range-ci-job-security-content/g" attack_range.conf + sed -i "s/automated_testing = .*$/automated_testing = 1/g" attack_range.conf + sed -i "s/github_token =.*$/github_token = ${GITHUB_TOKEN}/g" attack_range.conf + sed -i "s/region = us-west-2/region = us-west-2/g" attack_range.conf + sed -i "s/private_key_path =.*$/private_key_path = ~\/.ssh\/id_rsa_3968782b01d635eb784228581d9cbe90/g" attack_range.conf + sed -i "s/windows_domain_controller = .*$/windows_domain_controller = 1/g" attack_range.conf + sed -i "s/capture_attack_data = .*$/capture_attack_data = 1/g" attack_range.conf + cd terraform/ + terraform init + cd .. + python attack_range.py --mode terraform --action test --test_file ../security-content/tests/T1003_003.yml + - run: + name: detection tests > T1003_002.yml + command: | + cd attack_range + source venv/bin/activate + # change attack_range default ssh keys + sed -i "s/key_name = .*$/key_name = attack-range-ci-job-security-content/g" attack_range.conf + sed -i "s/automated_testing = .*$/automated_testing = 1/g" attack_range.conf + sed -i "s/github_token =.*$/github_token = ${GITHUB_TOKEN}/g" attack_range.conf + sed -i "s/region = us-west-2/region = us-west-2/g" attack_range.conf + sed -i "s/private_key_path =.*$/private_key_path = ~\/.ssh\/id_rsa_3968782b01d635eb784228581d9cbe90/g" attack_range.conf + sed -i "s/windows_domain_controller = .*$/windows_domain_controller = 1/g" attack_range.conf + sed -i "s/capture_attack_data = .*$/capture_attack_data = 1/g" attack_range.conf + cd terraform/ + terraform init + cd .. + python attack_range.py --mode terraform --action test --test_file ../security-content/tests/T1003_002.yml + - run: + name: detection tests > T1136_001.yml + command: | + cd attack_range + source venv/bin/activate + # change attack_range default ssh keys + sed -i "s/key_name = .*$/key_name = attack-range-ci-job-security-content/g" attack_range.conf + sed -i "s/automated_testing = .*$/automated_testing = 1/g" attack_range.conf + sed -i "s/github_token =.*$/github_token = ${GITHUB_TOKEN}/g" attack_range.conf + sed -i "s/region = us-west-2/region = us-west-2/g" attack_range.conf + sed -i "s/private_key_path =.*$/private_key_path = ~\/.ssh\/id_rsa_3968782b01d635eb784228581d9cbe90/g" attack_range.conf + sed -i "s/windows_domain_controller = .*$/windows_domain_controller = 1/g" attack_range.conf + sed -i "s/capture_attack_data = .*$/capture_attack_data = 1/g" attack_range.conf + cd terraform/ + terraform init + cd .. + python attack_range.py --mode terraform --action test --test_file ../security-content/tests/T1136_001.yml + - run: + name: detection tests > T1003_001.yml + command: | + cd attack_range + source venv/bin/activate + # change attack_range default ssh keys + sed -i "s/key_name = .*$/key_name = attack-range-ci-job-security-content/g" attack_range.conf + sed -i "s/automated_testing = .*$/automated_testing = 1/g" attack_range.conf + sed -i "s/github_token =.*$/github_token = ${GITHUB_TOKEN}/g" attack_range.conf + sed -i "s/region = us-west-2/region = us-west-2/g" attack_range.conf + sed -i "s/private_key_path =.*$/private_key_path = ~\/.ssh\/id_rsa_3968782b01d635eb784228581d9cbe90/g" attack_range.conf + sed -i "s/windows_domain_controller = .*$/windows_domain_controller = 1/g" attack_range.conf + sed -i "s/capture_attack_data = .*$/capture_attack_data = 1/g" attack_range.conf + cd terraform/ + terraform init + cd .. + python attack_range.py --mode terraform --action test --test_file ../security-content/tests/T1003_001.yml + - run: + name: detection tests > T1551_001.yml + command: | + cd attack_range + source venv/bin/activate + # change attack_range default ssh keys + sed -i "s/key_name = .*$/key_name = attack-range-ci-job-security-content/g" attack_range.conf + sed -i "s/automated_testing = .*$/automated_testing = 1/g" attack_range.conf + sed -i "s/github_token =.*$/github_token = ${GITHUB_TOKEN}/g" attack_range.conf + sed -i "s/region = us-west-2/region = us-west-2/g" attack_range.conf + sed -i "s/private_key_path =.*$/private_key_path = ~\/.ssh\/id_rsa_3968782b01d635eb784228581d9cbe90/g" attack_range.conf + sed -i "s/windows_domain_controller = .*$/windows_domain_controller = 1/g" attack_range.conf + sed -i "s/capture_attack_data = .*$/capture_attack_data = 1/g" attack_range.conf + cd terraform/ + terraform init + cd .. + python attack_range.py --mode terraform --action test --test_file ../security-content/tests/T1551_001.yml + - run: + name: cleanup terraform on failures + command: | + cd attack_range + source venv/bin/activate + python attack_range.py --mode terraform --action destroy + when: on_fail + validate-content: executor: content-executor steps: @@ -81,19 +258,11 @@ jobs: cd security-content source venv/bin/activate python bin/doc-gen.py --path . --output docs -v - - run: - name: check for broken links using liche - command: | - echo 'export GOROOT=~/.go' >> $BASH_ENV - echo 'export PATH=$GOROOT/bin:$PATH' >> $BASH_ENV - echo 'export GOPATH=~/go' >> $BASH_ENV - echo 'export PATH=$GOPATH/bin:$PATH' >> $BASH_ENV - echo 'export GO111MODULE="on"' >> $BASH_ENV - source $BASH_ENV - go get -u github.com/raviqqe/liche - cd security-content - liche docs/stories_categories.md -v -t 45 - liche README.md -v -t 45 + - slack/status: + webhook: '${SLACK_WEBHOOK}' + fail_only: true + + build-sources: executor: content-executor steps: @@ -127,7 +296,7 @@ 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 @@ -159,6 +328,10 @@ jobs: root: security-content/ paths: - content-pack-build.tar.gz + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + build-package: executor: content-executor steps: @@ -196,6 +369,10 @@ jobs: root: ~/dist paths: - DA-ESS-ContentUpdate-latest.tar.gz + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + run-appinspect: executor: content-executor steps: @@ -228,6 +405,10 @@ jobs: root: ~/ paths: - DA-ESS-ContentUpdate-latest.tar.gz + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + community-api-update: executor: aws-cli/default steps: @@ -244,17 +425,19 @@ 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: @@ -309,6 +492,10 @@ jobs: root: ~/ paths: - DA-ESS-ContentUpdate-latest.tar.gz + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + publish-github-release: docker: - image: cibuilds/github:0.10 @@ -324,6 +511,10 @@ jobs: root: ~/ paths: - DA-ESS-ContentUpdate-latest.tar.gz + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + attack-range-update: executor: aws-cli/default steps: @@ -334,9 +525,13 @@ jobs: - run: name: sync latest ESCU to the Attack Range S3 bucket for apps command: | - aws s3 cp ~/DA-ESS-ContentUpdate-latest.tar.gz s3://attack-range-appbinaries/ + aws s3 cp ~/DA-ESS-ContentUpdate-latest.tar.gz s3://attack-range-appbinaries/ # make the file public since it is not by default aws s3api put-object-acl --bucket attack-range-appbinaries --key DA-ESS-ContentUpdate-latest.tar.gz --acl public-read + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + master-api-update: executor: aws-cli/default steps: @@ -351,19 +546,35 @@ 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" + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + workflows: version: 2.1 + test-detections: + jobs: + - test-detections + triggers: + - schedule: + cron: "0 5 * * *" + filters: + branches: + only: + - develop validate-and-build: jobs: - validate-content: @@ -458,4 +669,4 @@ workflows: tags: only: /^v.*/ branches: - ignore: /.*/ + ignore: /.*/ \ No newline at end of file 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 d5ae5b7ada..b8a21cf74f 100644 --- a/README.md +++ b/README.md @@ -1 +1,84 @@ -new version of the README needs to be done + + + +# Splunk Security Content +![security-content](docs/static/logo.png) +===== + +| branch | build status | +| --- | --- | +| 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 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 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 instance. + +#### [API](https://docs.splunkresearch.com/?version=latest) +``` +curl -s https://content.splunkresearch.com | jq +{ + "hello": "welcome to Splunks Research security content api" +} +``` + +#### [GitHub Workflow](https://github.com/splunk/security-content/wiki/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). + +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. + +**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. A good example + + +# Execute an Analytic Story + +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 Content](https://github.com/splunk/security-content/wiki/Developing-Content). + +# Security Content + +#### Content Parts +* [stories/](stories/): All Analytic Stories +* [detections/](detections/): Splunk Enterprise, Splunk UBA, and Splunk Phantom detections that power 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 + +#### Supporting Parts +* [package/](package/): Splunk content app-source files, including lookups, binaries, and default config files +* [bin/](bin/): All binaries required to produce and test content + +# Contribution +We welcome feedback and contributions from the community! Please see our [contributing to the project](https://github.com/splunk/security-content/wiki/Contributing-to-the-Project) 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. + +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 diff --git a/_config.yml b/_config.yml deleted file mode 100644 index fc24e7a62d..0000000000 --- a/_config.yml +++ /dev/null @@ -1 +0,0 @@ -theme: jekyll-theme-hacker \ No newline at end of file diff --git a/baselines/baseline_of_command_line_length___mltk.yml b/baselines/baseline_of_command_line_length___mltk.yml index f5068fa341..28205e56d9 100644 --- a/baselines/baseline_of_command_line_length___mltk.yml +++ b/baselines/baseline_of_command_line_length___mltk.yml @@ -31,5 +31,5 @@ tags: - Suspicious MSHTA Activity - Unusual Processes detections: - - Unusually Long Command Line - MLTK - Detect Prohibited Applications Spawning cmd.exe + - Unusually Long Command Line - MLTK diff --git a/baselines/baseline_of_smb_traffic___mltk.yml b/baselines/baseline_of_smb_traffic___mltk.yml index f852ea17ce..e5b850a63c 100644 --- a/baselines/baseline_of_smb_traffic___mltk.yml +++ b/baselines/baseline_of_smb_traffic___mltk.yml @@ -36,5 +36,5 @@ tags: - Netsh Abuse - Ransomware detections: - - SMB Traffic Spike - MLTK - Processes launching netsh + - SMB Traffic Spike - MLTK diff --git a/baselines/dnstwist_domain_names.yml b/baselines/dnstwist_domain_names.yml index 7955295932..dde08528b5 100644 --- a/baselines/dnstwist_domain_names.yml +++ b/baselines/dnstwist_domain_names.yml @@ -17,5 +17,5 @@ tags: - Suspicious Emails detections: - Monitor Email For Brand Abuse - - Monitor Web Traffic For Brand Abuse - Monitor DNS For Brand Abuse + - Monitor Web Traffic For Brand Abuse diff --git a/baselines/previously_seen_aws_provisioning_activity_sources.yml b/baselines/previously_seen_aws_provisioning_activity_sources.yml index 24fad13984..c4eec20fd3 100644 --- a/baselines/previously_seen_aws_provisioning_activity_sources.yml +++ b/baselines/previously_seen_aws_provisioning_activity_sources.yml @@ -17,7 +17,7 @@ tags: analytics_story: - AWS Suspicious Provisioning Activities detections: - - AWS Cloud Provisioning From Previously Unseen City - AWS Cloud Provisioning From Previously Unseen IP Address - - AWS Cloud Provisioning From Previously Unseen Region + - 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_command_line_arguments.yml b/baselines/previously_seen_command_line_arguments.yml index 32d364cc1e..8b15521edb 100644 --- a/baselines/previously_seen_command_line_arguments.yml +++ b/baselines/previously_seen_command_line_arguments.yml @@ -25,6 +25,6 @@ tags: - Suspicious Command-Line Executions - Suspicious MSHTA Activity detections: + - Detect Prohibited Applications Spawning cmd.exe - Processes launching netsh - First time seen command line argument - - Detect Prohibited Applications Spawning cmd.exe diff --git a/baselines/previously_seen_running_windows_services.yml b/baselines/previously_seen_running_windows_services.yml index 457c380a0f..37950e87a4 100644 --- a/baselines/previously_seen_running_windows_services.yml +++ b/baselines/previously_seen_running_windows_services.yml @@ -1,20 +1,23 @@ -name: Previously Seen Running Windows Services +name: Previously Seen Running Windows Services - Initial id: 64ce0ade-cb01-4678-bddd-d31c0b175394 -version: 2 -date: '2020-01-13' +version: 3 +date: '2020-06-23' 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. + Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 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' +search: '`wineventlog_system` EventCode=7036 | + rex field=Message "The (?[-\(\)\s\w]+) service entered the (?\w+) state" | + where state="running" | + stats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen by service | + outputlookup previously_seen_running_windows_services' tags: analytics_story: - Orangeworm Attack Group - Windows Service Abuse detections: - First Time Seen Running Windows Service + deployments: + - 90 Day Baseline diff --git a/baselines/previously_seen_running_windows_services_update.yml b/baselines/previously_seen_running_windows_services_update.yml new file mode 100644 index 0000000000..529ad04e56 --- /dev/null +++ b/baselines/previously_seen_running_windows_services_update.yml @@ -0,0 +1,28 @@ +name: Previously Seen Running Windows Services - Update +id: 2e3bdd68-1863-46ee-81f8-87273eee7f1c +version: 3 +date: '2020-06-23' +description: This search returns the first and last time a Windows service was seen across your enterprise + within the last hour. It then updates this information with historical data and filters out + Windows services pairs that have not been seen within the specified time window. + This updated table is then cached. +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 8.0.0 or above. +author: David Dorsey, Splunk +search: '`wineventlog_system` EventCode=7036 | + rex field=Message "The (?[-\(\)\s\w]+) service entered the (?\w+) state" | + where state="running" | + stats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen by service | + inputlookup previously_seen_running_windows_services append=t | + stats min(firstTimeSeen) as firstTimeSeen, max(lastTimeSeen) as lastTimeSeen by service | + where lastTimeSeen > relative_time(now(), "`previously_seen_windows_service_forget_window`") | + outputlookup previously_seen_running_windows_services' +tags: + analytics_story: + - Orangeworm Attack Group + - Windows Service Abuse + detections: + - First Time Seen Running Windows Service + deployments: + - Hourly Cache Updates diff --git a/baselines/previously_seen_users_in_cloudtrail.yml b/baselines/previously_seen_users_in_cloudtrail.yml index 6f5a11191f..0a75480cd8 100644 --- a/baselines/previously_seen_users_in_cloudtrail.yml +++ b/baselines/previously_seen_users_in_cloudtrail.yml @@ -20,6 +20,6 @@ tags: - Suspicious AWS Login Activities detections: - Detect AWS Console Login by User from New Country - - 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 City - Detect new user AWS Console Login diff --git a/baselines/previously_seen_users_in_cloudtrail___dm.yml b/baselines/previously_seen_users_in_cloudtrail___dm.yml new file mode 100644 index 0000000000..2e9c19a865 --- /dev/null +++ b/baselines/previously_seen_users_in_cloudtrail___dm.yml @@ -0,0 +1,25 @@ +name: Previously seen users in CloudTrail - DM +id: 0a87ecf9-dc6a-43af-861a-205e75a09bf5 +version: 1 +date: '2020-05-28' +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 username, within the last 30 + days. +how_to_implement: You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) + and Enterprise Security 6.2, which contains the required updates to the Authentication data model + for cloud use cases. Validate the user name entries in `previously_seen_users_console_logins.csv`, + which is a lookup file created by this support search. +author: Rico Valdez, Splunk +search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication + where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src + | iplocation Authentication.src | rename Authentication.user as user Authentication.src as src + | table user src City Region Country firstTime lastTime | outputlookup previously_seen_users_console_logins.csv | stats count' +tags: + analytics_story: + - Suspicious Cloud Authentication 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 - DM diff --git a/baselines/previously_seen_zoom_child_processes_initial.yml b/baselines/previously_seen_zoom_child_processes_initial.yml new file mode 100644 index 0000000000..2ee4120525 --- /dev/null +++ b/baselines/previously_seen_zoom_child_processes_initial.yml @@ -0,0 +1,22 @@ +name: Previously Seen Zoom Child Processes - Initial +id: 60b9c00f-a9d6-4e51-803c-5d63ea21b95b +version: 1 +date: '2020-05-20' +description: This search returns the first and last time a process was seen per endpoint with + a parent process of zoom.exe (Windows) or zoom.us (macOS). This table is then cached. +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. +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` min(_time) as firstTimeSeen max(_time) as lastTimeSeen + from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) + by Processes.process_name Processes.dest| `drop_dm_object_name(Processes)` + | table dest, process_name, firstTimeSeen, lastTimeSeen + | outputlookup zoom_first_time_child_process' +tags: + analytics_story: + - Suspicious Zoom Child Processes + detections: + - First Time Seen Child Process of Zoom + deployments: + - 90 Day Baseline diff --git a/baselines/previously_seen_zoom_child_processes_update.yml b/baselines/previously_seen_zoom_child_processes_update.yml new file mode 100644 index 0000000000..048966f719 --- /dev/null +++ b/baselines/previously_seen_zoom_child_processes_update.yml @@ -0,0 +1,27 @@ +name: Previously Seen Zoom Child Processes - Update +id: 80aea7fd-5da2-4533-b3c2-560533bfbaee +version: 1 +date: '2020-05-20' +description: This search returns the first and last time a process was seen per endpoint with + a parent process of zoom.exe (Windows) or zoom.us (macOS) within the last hour. It then updates + this information with historical data and filters out proces_name and endpoint pairs that have not + been seen within the specified time window. This updated table is outputed to disk. +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. +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` min(_time) as firstTimeSeen max(_time) as lastTimeSeen + from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) + by Processes.process_name Processes.dest| `drop_dm_object_name(Processes)` + | table firstTimeSeen, lastTimeSeen, process_name, dest + | inputlookup zoom_first_time_child_process append=t + | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by process_name, dest + | where lastTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_forget_window`") + | outputlookup zoom_first_time_child_process' +tags: + analytics_story: + - Suspicious Zoom Child Processes + detections: + - First Time Seen Child Process of Zoom + deployments: + - Hourly Cache Updates diff --git a/baselines/update_previously_seen_users_in_cloudtrail.yml b/baselines/update_previously_seen_users_in_cloudtrail.yml index ca2d520640..cc8a111d73 100644 --- a/baselines/update_previously_seen_users_in_cloudtrail.yml +++ b/baselines/update_previously_seen_users_in_cloudtrail.yml @@ -21,6 +21,6 @@ tags: - Suspicious AWS Login Activities detections: - Detect AWS Console Login by User from New Country - - 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 City - Detect new user AWS Console Login diff --git a/baselines/update_previously_seen_users_in_cloudtrail___dm.yml b/baselines/update_previously_seen_users_in_cloudtrail___dm.yml new file mode 100644 index 0000000000..79a2e22a13 --- /dev/null +++ b/baselines/update_previously_seen_users_in_cloudtrail___dm.yml @@ -0,0 +1,27 @@ +name: Update previously seen users in CloudTrail - DM +id: 66ff71c2-7e01-47dd-a041-906688c9d322 +version: 1 +date: '2020-05-28' +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 user, within the last hour. +how_to_implement: You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) + and Enterprise Security 6.2, which contains the required updates to the Authentication data model + for cloud use cases. Validate the user name entries in `previously_seen_users_console_logins.csv`, + which is a lookup file created by this support search. +author: Rico Valdez, Splunk +search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from + datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user + Authenticaiton.src | iplocation Authentication.src | rename Authentication.user as user + Authentciation.src as src | table user src City Region Country firstTime lastTime + | 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 Cloud Authentication 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 - DM diff --git a/bin/ci-generate.py b/bin/ci-generate.py new file mode 100644 index 0000000000..9ce79a14dc --- /dev/null +++ b/bin/ci-generate.py @@ -0,0 +1,74 @@ +#!/usr/bin/python + +''' +Generates circleci jobs from the tests under /tests in the security-content repo. +''' + +import glob +import yaml +import argparse +from os import path +import sys +import datetime +from jinja2 import Environment, FileSystemLoader +import re +import os + + +# global variables +REPO_PATH = '' +VERBOSE = False +OUTPUT_PATH = '' + +# def load_objects(file_path, VERBOSE): +# files = [] +# test_files = path.join(path.expanduser(REPO_PATH), file_path) +# for file in sorted(glob.glob(test_files)): +# if VERBOSE: +# print("processing test: {0}".format(file)) +# 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 generate_circleci_conf(tests, OUTPUT_PATH, VERBOSE): + j2_env = Environment(loader=FileSystemLoader('bin/jinja2_templates'), + trim_blocks=True) + template = j2_env.get_template('circleci_config.j2') + output_path = OUTPUT_PATH + "config.yml" + utc_time = datetime.datetime.utcnow().replace(microsecond=0).isoformat() + output = template.render(detection_tests=tests, time=utc_time) + with open(output_path, 'w') as f: + f.write(output) + + +if __name__ == "__main__": + + parser = argparse.ArgumentParser(description="generates circleci config file from tests", epilog=""" + This generates circleci config files that includes all of our CI logic as well as a dynamically created set of detection tests. + The detection tests are defined under the /tests folder on the security-content repository. + The config file is saved under the .circleci/config.yml file.""") + parser.add_argument("-p", "--path", required=False, default=".", help="path to security-content repo, defaults to: .") + parser.add_argument("-o", "--output", required=False, default=".circleci/", help="path to the output directory of circleci config, defaults to: .circleci/") + parser.add_argument("-v", "--verbose", required=False, default=False, action='store_true', help="prints verbose output") + + # parse them + args = parser.parse_args() + REPO_PATH = args.path + OUTPUT_PATH = args.output + VERBOSE = args.verbose + #tests = load_objects("tests/*.yml", VERBOSE) + tests = arr = os.listdir("tests/") + generate_circleci_conf(tests, OUTPUT_PATH, VERBOSE) + if VERBOSE: + print("{0} tests have been successfully written to {1}".format(len(tests), OUTPUT_PATH)) + print("security content circleci config generation completed..") diff --git a/bin/generate.py b/bin/generate.py index cd28d011d1..54fc9781eb 100644 --- a/bin/generate.py +++ b/bin/generate.py @@ -12,6 +12,8 @@ import sys import datetime from jinja2 import Environment, FileSystemLoader import re +from attackcti import attack_client +import csv # global variables @@ -19,14 +21,13 @@ REPO_PATH = '' 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 sorted(glob.glob(manifest_files)): + if VERBOSE: + print("processing manifest: {0}".format(file)) files.append(load_file(file)) - return files @@ -55,6 +56,22 @@ def generate_transforms_conf(lookups): return output_path +def generate_collections_conf(lookups): + filtered_lookups = list(filter(lambda i: 'collection' in i, lookups)) + sorted_lookups = sorted(filtered_lookups, key=lambda i: i['name']) + + utc_time = datetime.datetime.utcnow().replace(microsecond=0).isoformat() + + j2_env = Environment(loader=FileSystemLoader('bin/jinja2_templates'), + trim_blocks=True) + template = j2_env.get_template('collections.j2') + output_path = OUTPUT_PATH + "/default/collections.conf" + output = template.render(lookups=sorted_lookups, time=utc_time) + with open(output_path, 'w') as f: + f.write(output) + + return output_path + def generate_savedsearches_conf(detections, response_tasks, baselines, deployments): @@ -150,6 +167,7 @@ def generate_use_case_library_conf(stories, detections, response_tasks, baseline sto_res = map_response_tasks_to_stories(response_tasks) for story in stories: + story['author_name'], story['author_company'] = parse_author_company(story) if story['name'] in sto_det: story['detections'] = list(sto_det[story['name']]) if story['name'] in sto_res: @@ -235,6 +253,12 @@ def generate_workbench_panels(response_tasks, stories): trim_blocks=True) template = j2_env.get_template('panel.j2') output_path = OUTPUT_PATH + "/default/data/ui/panels/workbench_panel_" + response_file_name + ".xml" + + if response_task['search'].find(">") is not -1: + response_task['search']= response_task['search'].replace(">",">") + if response_task['search'].find("<") is not -1: + response_task['search']= response_task['search'].replace("<","<") + output = template.render(search=response_task['search']) with open(output_path, 'w') as f: f.write(output) @@ -263,6 +287,22 @@ def parse_data_models_from_search(search): return False +def parse_author_company(story): + match_author = re.search(r'^([^,]+)', story['author']) + if match_author is None: + match_author = 'no' + else: + match_author = match_author.group(1) + + match_company = re.search(r',\s?(.*)$', story['author']) + if match_company is None: + match_company = 'no' + else: + match_company = match_company.group(1) + + return match_author, match_company + + def get_deployments(object, deployments): matched_deployments = [] @@ -436,6 +476,33 @@ def prepare_stories(stories, detections): return stories +def generate_mitre_lookup(): + + csv_mitre_rows = [["mitre_id", "technique", "tactics", "groups"]] + + lift = attack_client() + all_enterprise = lift.get_enterprise(stix_format=False) + enterprise_relationships = lift.get_enterprise_relationships() + enterprise_groups = lift.get_enterprise_groups() + + 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']) + + 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)]) + + with open('lookups/mitre_enrichment.csv', 'w', newline='') as file: + writer = csv.writer(file) + writer.writerows(csv_mitre_rows) + + + if __name__ == "__main__": parser = argparse.ArgumentParser(description="generates splunk conf files out of security-content manifests", epilog=""" @@ -450,16 +517,24 @@ if __name__ == "__main__": REPO_PATH = args.path OUTPUT_PATH = args.output VERBOSE = args.verbose - stories = load_objects("stories/*.yml") - macros = load_objects("macros/*.yml") - lookups = load_objects("lookups/*.yml") - baselines = load_objects("baselines/*.yml") - detections = load_objects("detections/*.yml") - responses = load_objects("responses/*.yml") - response_tasks = load_objects("response_tasks/*.yml") - deployments = load_objects("deployments/*.yml") + 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) + + try: + if VERBOSE: + print("generating Mitre lookups") + generate_mitre_lookup() + except: + print("WARNING: Generation of Mitre lookup failed.") lookups_path = generate_transforms_conf(lookups) + lookups_path = generate_collections_conf(lookups) detections = sorted(detections, key=lambda d: d['name']) response_tasks = sorted(response_tasks, key=lambda i: i['name']) @@ -476,6 +551,7 @@ if __name__ == "__main__": generate_workbench_panels(response_tasks, stories) + if VERBOSE: print("{0} stories have been successfully written to {1}".format(len(stories), story_path)) print("{0} detections have been successfully written to {1}".format(len(detections), detection_path)) diff --git a/bin/jinja2_templates/circleci_config.j2 b/bin/jinja2_templates/circleci_config.j2 new file mode 100644 index 0000000000..b0bc866559 --- /dev/null +++ b/bin/jinja2_templates/circleci_config.j2 @@ -0,0 +1,572 @@ +# Python CircleCI 2.0 configuration file +# +# Check https://circleci.com/docs/2.0/language-python/ for more details +# +############# +# Automatically generated by ci-generator.py in splunk/security-content +# On Date: {{ time }} UTC +# Author: Splunk Security Research +# Contact: research@splunk.com +############# + +version: 2.1 +orbs: + aws-cli: circleci/aws-cli@0.1.19 + slack: circleci/slack@3.4.2 + +dependencies: + cache_directories: + - "~/.apt-cache" + pre: + - sudo rm -rf /var/cache/apt/archives && sudo ln -s ~/.apt-cache /var/cache/apt/archives && mkdir -p ~/.apt-cache/partial + +apt-run: &apt-install + name: install system packages + command: | + sudo apt update -qq + sudo apt install -y python-dev python3-dev -qq + # install go for other testing tools + # but first lets clean up the env + if [ -f goinstall.sh ]; then + rm goinstall.sh + fi + if [ -d ~/.go ]; then + rm -rf ~/.go + fi + wget https://raw.githubusercontent.com/canha/golang-tools-install-script/master/goinstall.sh + sudo chown circleci goinstall.sh + chmod +x goinstall.sh + ./goinstall.sh + +executors: + content-executor: + docker: + - image: circleci/python:latest + working_directory: ~/repo + +jobs: + test-detections: + executor: content-executor + steps: + - run: + name: checkout repo + command: | + if [[ ! -z "${CIRCLE_PULL_REQUEST}" && ! -z "${CIRCLE_PR_NUMBER}" ]]; then + git clone https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + cd security-content + git fetch origin pull/${CIRCLE_PR_NUMBER}/head:${CIRCLE_BRANCH} + git checkout ${CIRCLE_BRANCH} + elif [ "${CIRCLE_BRANCH}" == "" ]; then + git clone https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + else + git clone --branch ${CIRCLE_BRANCH} https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + fi + - run: + name: checkout attack_range repo + command: | + git clone https://${GITHUB_TOKEN}@github.com/splunk/attack_range.git + - restore_cache: + key: virtualenv + - run: *apt-install + - run: + name: install python dependencies + command: | + cd attack_range + rm -rf venv + virtualenv --python=/usr/bin/python3 --clear venv + source venv/bin/activate + pip install -q -r requirements.txt + - aws-cli/setup: + profile-name: default + - add_ssh_keys: + fingerprints: + - "39:68:78:2b:01:d6:35:eb:78:42:28:58:1d:9c:be:90" + - run: + name: install terraform + command: | + wget https://releases.hashicorp.com/terraform/0.12.24/terraform_0.12.24_linux_amd64.zip + sudo unzip terraform_0.12.24_linux_amd64.zip -d /usr/bin + rm terraform_0.12.24_linux_amd64.zip + terraform --version +{% for test in detection_tests %} + - run: + name: detection tests > {{ test }} + command: | + cd attack_range + source venv/bin/activate + # change attack_range default ssh keys + sed -i "s/key_name = .*$/key_name = attack-range-ci-job-security-content/g" attack_range.conf + sed -i "s/automated_testing = .*$/automated_testing = 1/g" attack_range.conf + sed -i "s/github_token =.*$/github_token = ${GITHUB_TOKEN}/g" attack_range.conf + sed -i "s/region = us-west-2/region = us-west-2/g" attack_range.conf + sed -i "s/private_key_path =.*$/private_key_path = ~\/.ssh\/id_rsa_3968782b01d635eb784228581d9cbe90/g" attack_range.conf + sed -i "s/windows_domain_controller = .*$/windows_domain_controller = 1/g" attack_range.conf + sed -i "s/capture_attack_data = .*$/capture_attack_data = 1/g" attack_range.conf + cd terraform/ + terraform init + cd .. + python attack_range.py --mode terraform --action test --test_file ../security-content/tests/{{ test }} +{% endfor %} + - run: + name: cleanup terraform on failures + command: | + cd attack_range + source venv/bin/activate + python attack_range.py --mode terraform --action destroy + when: on_fail + + validate-content: + executor: content-executor + steps: + - run: + name: checkout repo + command: | + if [[ ! -z "${CIRCLE_PULL_REQUEST}" && ! -z "${CIRCLE_PR_NUMBER}" ]]; then + git clone https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + cd security-content + git fetch origin pull/${CIRCLE_PR_NUMBER}/head:${CIRCLE_BRANCH} + git checkout ${CIRCLE_BRANCH} + elif [ "${CIRCLE_BRANCH}" == "" ]; then + git clone https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + else + git clone --branch ${CIRCLE_BRANCH} https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + fi + - restore_cache: + key: virtualenv + - run: *apt-install + - run: + name: install python dependencies + command: | + cd security-content + rm -rf venv + virtualenv --python=/usr/bin/python3 --clear venv + source venv/bin/activate + pip install -q -r requirements.txt + - save_cache: + key: virtualenv + paths: + - "/security-content/venv" + - run: + name: run validate + command: | + cd security-content + source venv/bin/activate + python bin/validate.py --path . --verbose + - run: + name: run doc-gen + command: | + cd security-content + source venv/bin/activate + python bin/doc-gen.py --path . --output docs -v + - slack/status: + webhook: '${SLACK_WEBHOOK}' + fail_only: true + + + build-sources: + executor: content-executor + steps: + - run: + name: checkout repo + command: | + if [[ ! -z "${CIRCLE_PULL_REQUEST}" && ! -z "${CIRCLE_PR_NUMBER}" ]]; then + git clone https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + cd security-content + git fetch origin pull/${CIRCLE_PR_NUMBER}/head:${CIRCLE_BRANCH} + git checkout ${CIRCLE_BRANCH} + elif [ "${CIRCLE_BRANCH}" == "" ]; then + git clone https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + else + git clone --branch ${CIRCLE_BRANCH} https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + fi + - run: *apt-install + - run: + name: install python dependencies + command: | + cd security-content + rm -rf venv + virtualenv --python=/usr/bin/python3 --clear venv + source venv/bin/activate + pip install -q -r requirements.txt + - run: + name: run generate + command: | + cd security-content + source venv/bin/activate + 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: + name: copy lookups .csv files + command: | + cd security-content + # clean up current lookups + rm -rf package/lookups + mkdir package/lookups + # copy over lookups + cd lookups + cp -rv *.csv ../package/lookups + - run: + name: update version and build number + command: | + cd security-content + # check if tag is set, get build number from the tag if set + if [ -z "${CIRCLE_TAG}" ]; then + CONTENT_VERSION=$(grep -oP "(\d.\d.\d+$)" package/default/content-version.conf) + echo "detected content version: $CONTENT_VERSION" + else + CONTENT_VERSION=$(echo $CIRCLE_TAG | grep -oP "\d.\d.\d+") + echo "content version: $CONTENT_VERSION, set by tag: $CIRCLE_TAG" + fi + # update build number and version + sed -i "s/build = .*$/build = $CIRCLE_BUILD_NUM/g" package/default/app.conf + sed -i "s/^version = .*$/version = $CONTENT_VERSION/g" package/default/app.conf + sed -i "s/\"version\": .*$/\"version\": \"$CONTENT_VERSION\"/g" package/app.manifest + sed -i "s/version = .*$/version = $CONTENT_VERSION/g" package/default/content-version.conf + tar -czf content-pack-build.tar.gz package/* + - persist_to_workspace: + root: security-content/ + paths: + - content-pack-build.tar.gz + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + + build-package: + executor: content-executor + steps: + - attach_workspace: + at: ~/dist + - run: + name: grab splunk packaging toolkit + command: | + curl -Ls https://download.splunk.com/misc/packaging-toolkit/splunk-packaging-toolkit-0.9.0.tar.gz -o ~/splunk-packaging-toolkit-latest.tar.gz + mkdir ~/slim-latest + tar -zxf ~/splunk-packaging-toolkit-latest.tar.gz -C ~/slim-latest --strip-components=1 + - run: + name: install splunk packaging toolkit (slim) + command: | + cd ~/slim-latest + sudo pip install --upgrade pip setuptools + sudo pip install virtualenv + virtualenv --python=/usr/bin/python2.7 --clear venv + source venv/bin/activate + pip install semantic_version + pip install . + - run: + name: create a .spl for this build using slim + command: | + source ~/slim-latest/venv/bin/activate + cd ~/dist + tar -zxf content-pack-build.tar.gz + mv package DA-ESS-ContentUpdate + slim package -o upload DA-ESS-ContentUpdate + cp upload/*.tar.gz DA-ESS-ContentUpdate-latest.tar.gz + - store_artifacts: + path: ~/dist/upload + destination: package/ + - persist_to_workspace: + root: ~/dist + paths: + - DA-ESS-ContentUpdate-latest.tar.gz + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + + run-appinspect: + executor: content-executor + steps: + - attach_workspace: + at: ~/ + - run: *apt-install + - run: + name: grab appinspect + command: | + curl -Ls https://download.splunk.com/misc/appinspect/splunk-appinspect-2.0.0.tar.gz -o appinspect-lastest.tar.gz + mkdir appinspect-latest + tar -zxf appinspect-lastest.tar.gz -C appinspect-latest --strip-components=1 + - run: + name: install app inspect + command: | + cd appinspect-latest + rm -rf venv + sudo pip install --upgrade pip setuptools + sudo pip install virtualenv + virtualenv --python=/usr/bin/python3 --clear venv + source venv/bin/activate + pip install . + - run: + name: run app inspect + command: | + cd appinspect-latest + source venv/bin/activate + splunk-appinspect inspect ~/DA-ESS-ContentUpdate-latest.tar.gz --included-tags=cloud --max-messages=all + - persist_to_workspace: + root: ~/ + paths: + - DA-ESS-ContentUpdate-latest.tar.gz + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + + community-api-update: + executor: aws-cli/default + steps: + - attach_workspace: + at: ~/ + - run: + name: checkout repo + command: | + if [ "${CIRCLE_BRANCH}" == "" ]; then + git clone community https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + else + git clone --branch ${CIRCLE_BRANCH} https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + fi + - run: *apt-install + - aws-cli/setup: + profile-name: default + - run: + name: update community 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 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: + - DA-ESS-ContentUpdate-latest.tar.gz + update-sources-github: + executor: content-executor + steps: + - attach_workspace: + at: ~/ + - run: *apt-install + - run: + name: checkout repo + command: | + mkdir ~/latest + tar -zxf ~/DA-ESS-ContentUpdate-latest.tar.gz -C ~/latest --strip-components=1 + if [ "${CIRCLE_BRANCH}" == "" ]; then + git clone https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + else + git clone --branch ${CIRCLE_BRANCH} https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + fi + - run: + name: install python dependencies + command: | + cd security-content + rm -rf venv + virtualenv --python=/usr/bin/python3 --clear venv + source venv/bin/activate + pip install -q -r requirements.txt + - run: + name: run doc-gen + command: | + cd security-content + source venv/bin/activate + python bin/doc-gen.py --path . --output docs -v + - run: + name: update github with new docs and package bits + command: | + cd security-content + rm -rf package + mv ~/latest package + # configure git to prep for commit + git config credential.helper 'cache --timeout=120' + git config user.email "research@splunk.com" + git config user.name "research bot" + git config --global push.default simple + git add package/* + git add docs/* + git commit --allow-empty -m "updating docs and package bits [ci skip]" + # Push quietly to prevent showing the token in log + git push https://${GITHUB_TOKEN}@github.com/splunk/security-content.git ${CIRCLE_BRANCH} + - persist_to_workspace: + root: ~/ + paths: + - DA-ESS-ContentUpdate-latest.tar.gz + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + + publish-github-release: + docker: + - image: cibuilds/github:0.10 + steps: + - attach_workspace: + at: ~/ + - run: + name: publish release on github + command: | + cp ~/DA-ESS-ContentUpdate-latest.tar.gz ~/DA-ESS-ContentUpdate-${CIRCLE_TAG}.tar.gz + ghr -t ${GITHUB_TOKEN} -u ${CIRCLE_PROJECT_USERNAME} -r ${CIRCLE_PROJECT_REPONAME} -c ${CIRCLE_SHA1} -delete ${CIRCLE_TAG} ~/DA-ESS-ContentUpdate-${CIRCLE_TAG}.tar.gz + - persist_to_workspace: + root: ~/ + paths: + - DA-ESS-ContentUpdate-latest.tar.gz + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + + attack-range-update: + executor: aws-cli/default + steps: + - attach_workspace: + at: ~/ + - aws-cli/setup: + profile-name: default + - run: + name: sync latest ESCU to the Attack Range S3 bucket for apps + command: | + aws s3 cp ~/DA-ESS-ContentUpdate-latest.tar.gz s3://attack-range-appbinaries/ + # make the file public since it is not by default + aws s3api put-object-acl --bucket attack-range-appbinaries --key DA-ESS-ContentUpdate-latest.tar.gz --acl public-read + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + + master-api-update: + executor: aws-cli/default + steps: + - run: + name: checkout repo + command: | + if [ "${CIRCLE_BRANCH}" == "" ]; then + git clone https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + else + git clone --branch ${CIRCLE_BRANCH} https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + fi + - run: *apt-install + - aws-cli/setup: + profile-name: default + - 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 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" + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + +workflows: + version: 2.1 + test-detections: + jobs: + - test-detections + triggers: + - schedule: + cron: "0 5 * * *" + filters: + branches: + only: + - develop + validate-and-build: + jobs: + - validate-content: + # build always + filters: + tags: + only: /.*/ + - build-sources: + # build always + requires: + - validate-content + filters: + tags: + only: /.*/ + - build-package: + # build always + requires: + - validate-content + - build-sources + filters: + tags: + only: /.*/ + - run-appinspect: + # build always + requires: + - validate-content + - build-sources + - build-package + filters: + tags: + only: /.*/ + - community-api-update: + # build only on community changes + requires: + - validate-content + - build-sources + - build-package + - run-appinspect + filters: + branches: + only: community + - update-sources-github: + # update package and docs in github if is a tag + requires: + - validate-content + - build-sources + - build-package + - run-appinspect + filters: + tags: + only: /^v.*/ + branches: + ignore: /.*/ + - publish-github-release: + # publish release in github if is a tag + requires: + - validate-content + - build-sources + - build-package + - run-appinspect + - update-sources-github + filters: + tags: + only: /^v.*/ + branches: + ignore: /.*/ + - attack-range-update: + # update the attack range with the latest build + requires: + - validate-content + - build-sources + - build-package + - run-appinspect + - update-sources-github + - publish-github-release + filters: + tags: + only: /^v.*/ + branches: + ignore: /.*/ + - master-api-update: + # update master API from develop if is a tag + requires: + - validate-content + - build-sources + - build-package + - run-appinspect + - update-sources-github + - publish-github-release + - attack-range-update + filters: + tags: + only: /^v.*/ + branches: + ignore: /.*/ diff --git a/bin/jinja2_templates/circleci_config.j2.bcakup b/bin/jinja2_templates/circleci_config.j2.bcakup new file mode 100644 index 0000000000..193ee7df94 --- /dev/null +++ b/bin/jinja2_templates/circleci_config.j2.bcakup @@ -0,0 +1,575 @@ +# Python CircleCI 2.0 configuration file +# +# Check https://circleci.com/docs/2.0/language-python/ for more details +# +############# +# Automatically generated by ci-generator.py in splunk/security-content +# On Date: {{ time }} UTC +# Author: Splunk Security Research +# Contact: research@splunk.com +############# + +version: 2.1 +orbs: + aws-cli: circleci/aws-cli@0.1.19 + slack: circleci/slack@3.4.2 + +dependencies: + cache_directories: + - "~/.apt-cache" + pre: + - sudo rm -rf /var/cache/apt/archives && sudo ln -s ~/.apt-cache /var/cache/apt/archives && mkdir -p ~/.apt-cache/partial + +apt-run: &apt-install + name: install system packages + command: | + sudo apt update -qq + sudo apt install -y python-dev python3-dev -qq + # install go for other testing tools + # but first lets clean up the env + if [ -f goinstall.sh ]; then + rm goinstall.sh + fi + if [ -d ~/.go ]; then + rm -rf ~/.go + fi + wget https://raw.githubusercontent.com/canha/golang-tools-install-script/master/goinstall.sh + sudo chown circleci goinstall.sh + chmod +x goinstall.sh + ./goinstall.sh + +executors: + content-executor: + docker: + - image: circleci/python:latest + working_directory: ~/repo + +jobs: + test-detections: + executor: content-executor + steps: + - run: + name: checkout repo + command: | + if [[ ! -z "${CIRCLE_PULL_REQUEST}" && ! -z "${CIRCLE_PR_NUMBER}" ]]; then + git clone https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + cd security-content + git fetch origin pull/${CIRCLE_PR_NUMBER}/head:${CIRCLE_BRANCH} + git checkout ${CIRCLE_BRANCH} + elif [ "${CIRCLE_BRANCH}" == "" ]; then + git clone https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + else + git clone --branch ${CIRCLE_BRANCH} https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + fi + - run: + name: checkout attack_range repo + command: | + git clone --single-branch --branch automated_detection_testing https://${GITHUB_TOKEN}@github.com/splunk/attack_range.git + - restore_cache: + key: virtualenv + - run: *apt-install + - run: + name: install python dependencies + command: | + cd attack_range + rm -rf venv + virtualenv --python=/usr/bin/python3 --clear venv + source venv/bin/activate + pip install -q -r requirements.txt + - aws-cli/setup: + profile-name: default + - add_ssh_keys: + fingerprints: + - "39:68:78:2b:01:d6:35:eb:78:42:28:58:1d:9c:be:90" + - run: + name: install terraform + command: | + wget https://releases.hashicorp.com/terraform/0.12.24/terraform_0.12.24_linux_amd64.zip + sudo unzip terraform_0.12.24_linux_amd64.zip -d /usr/bin + rm terraform_0.12.24_linux_amd64.zip + terraform --version +{% for test in detection_tests %} + - run: + name: detection tests > {{ test.name }} + command: | + cd attack_range + source venv/bin/activate + # change attack_range default ssh keys + sed -i "s/key_name = .*$/key_name = attack-range-ci-job-security-content/g" attack_range.conf + sed -i "s/automated_testing = .*$/automated_testing = 1/g" attack_range.conf + sed -i "s/github_token = .*$/github_token = ${GITHUB_ACCESS_TOKEN}/g" attack_range.conf + sed -i "s/region = us-west-2/region = us-west-2/g" attack_range.conf + sed -i "s/private_key_path =.*$/private_key_path = ~\/.ssh\/id_rsa_3968782b01d635eb784228581d9cbe90/g" attack_range.conf + sed -i "s/windows_domain_controller = .*$/windows_domain_controller = 1/g" attack_range.conf + sed -i "s/capture_attack_data = .*$/capture_attack_data = 1/g" attack_range.conf + cat attack_range.conf + cd terraform/ + terraform init + cd .. + python attack_range.py --mode terraform --action test --test_file ../security-content/tests/{{ test.name|replace(" ", "_")|lower() }}.yml +{% endfor %} + - run: + name: cleanup terraform on failures + command: | + cd attack_range + source venv/bin/activate + python attack_range.py --mode terraform --action destroy + when: on_fail + + validate-content: + executor: content-executor + steps: + - run: + name: checkout repo + command: | + if [[ ! -z "${CIRCLE_PULL_REQUEST}" && ! -z "${CIRCLE_PR_NUMBER}" ]]; then + git clone https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + cd security-content + git fetch origin pull/${CIRCLE_PR_NUMBER}/head:${CIRCLE_BRANCH} + git checkout ${CIRCLE_BRANCH} + elif [ "${CIRCLE_BRANCH}" == "" ]; then + git clone https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + else + git clone --branch ${CIRCLE_BRANCH} https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + fi + - restore_cache: + key: virtualenv + - run: *apt-install + - run: + name: install python dependencies + command: | + cd security-content + rm -rf venv + virtualenv --python=/usr/bin/python3 --clear venv + source venv/bin/activate + pip install -q -r requirements.txt + - save_cache: + key: virtualenv + paths: + - "/security-content/venv" + - run: + name: run validate + command: | + cd security-content + source venv/bin/activate + python bin/validate.py --path . --verbose + - run: + name: run doc-gen + command: | + cd security-content + source venv/bin/activate + python bin/doc-gen.py --path . --output docs -v + - slack/status: + webhook: '${SLACK_WEBHOOK}' + fail_only: true + + + build-sources: + executor: content-executor + steps: + - run: + name: checkout repo + command: | + if [[ ! -z "${CIRCLE_PULL_REQUEST}" && ! -z "${CIRCLE_PR_NUMBER}" ]]; then + git clone https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + cd security-content + git fetch origin pull/${CIRCLE_PR_NUMBER}/head:${CIRCLE_BRANCH} + git checkout ${CIRCLE_BRANCH} + elif [ "${CIRCLE_BRANCH}" == "" ]; then + git clone https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + else + git clone --branch ${CIRCLE_BRANCH} https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + fi + - run: *apt-install + - run: + name: install python dependencies + command: | + cd security-content + rm -rf venv + virtualenv --python=/usr/bin/python3 --clear venv + source venv/bin/activate + pip install -q -r requirements.txt + - run: + name: run generate + command: | + cd security-content + source venv/bin/activate + 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: + name: copy lookups .csv files + command: | + cd security-content + # clean up current lookups + rm -rf package/lookups + mkdir package/lookups + # copy over lookups + cd lookups + cp -rv *.csv ../package/lookups + - run: + name: update version and build number + command: | + cd security-content + # check if tag is set, get build number from the tag if set + if [ -z "${CIRCLE_TAG}" ]; then + CONTENT_VERSION=$(grep -oP "(\d.\d.\d+$)" package/default/content-version.conf) + echo "detected content version: $CONTENT_VERSION" + else + CONTENT_VERSION=$(echo $CIRCLE_TAG | grep -oP "\d.\d.\d+") + echo "content version: $CONTENT_VERSION, set by tag: $CIRCLE_TAG" + fi + # update build number and version + sed -i "s/build = .*$/build = $CIRCLE_BUILD_NUM/g" package/default/app.conf + sed -i "s/^version = .*$/version = $CONTENT_VERSION/g" package/default/app.conf + sed -i "s/\"version\": .*$/\"version\": \"$CONTENT_VERSION\"/g" package/app.manifest + sed -i "s/version = .*$/version = $CONTENT_VERSION/g" package/default/content-version.conf + tar -czf content-pack-build.tar.gz package/* + - persist_to_workspace: + root: security-content/ + paths: + - content-pack-build.tar.gz + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + + build-package: + executor: content-executor + steps: + - attach_workspace: + at: ~/dist + - run: + name: grab splunk packaging toolkit + command: | + curl -Ls https://download.splunk.com/misc/packaging-toolkit/splunk-packaging-toolkit-0.9.0.tar.gz -o ~/splunk-packaging-toolkit-latest.tar.gz + mkdir ~/slim-latest + tar -zxf ~/splunk-packaging-toolkit-latest.tar.gz -C ~/slim-latest --strip-components=1 + - run: + name: install splunk packaging toolkit (slim) + command: | + cd ~/slim-latest + sudo pip install --upgrade pip setuptools + sudo pip install virtualenv + virtualenv --python=/usr/bin/python2.7 --clear venv + source venv/bin/activate + pip install semantic_version + pip install . + - run: + name: create a .spl for this build using slim + command: | + source ~/slim-latest/venv/bin/activate + cd ~/dist + tar -zxf content-pack-build.tar.gz + mv package DA-ESS-ContentUpdate + slim package -o upload DA-ESS-ContentUpdate + cp upload/*.tar.gz DA-ESS-ContentUpdate-latest.tar.gz + - store_artifacts: + path: ~/dist/upload + destination: package/ + - persist_to_workspace: + root: ~/dist + paths: + - DA-ESS-ContentUpdate-latest.tar.gz + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + + run-appinspect: + executor: content-executor + steps: + - attach_workspace: + at: ~/ + - run: *apt-install + - run: + name: grab appinspect + command: | + curl -Ls https://download.splunk.com/misc/appinspect/splunk-appinspect-2.0.0.tar.gz -o appinspect-lastest.tar.gz + mkdir appinspect-latest + tar -zxf appinspect-lastest.tar.gz -C appinspect-latest --strip-components=1 + - run: + name: install app inspect + command: | + cd appinspect-latest + rm -rf venv + sudo pip install --upgrade pip setuptools + sudo pip install virtualenv + virtualenv --python=/usr/bin/python3 --clear venv + source venv/bin/activate + pip install . + - run: + name: run app inspect + command: | + cd appinspect-latest + source venv/bin/activate + splunk-appinspect inspect ~/DA-ESS-ContentUpdate-latest.tar.gz --included-tags=cloud --max-messages=all + - persist_to_workspace: + root: ~/ + paths: + - DA-ESS-ContentUpdate-latest.tar.gz + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + + community-api-update: + executor: aws-cli/default + steps: + - attach_workspace: + at: ~/ + - run: + name: checkout repo + command: | + if [ "${CIRCLE_BRANCH}" == "" ]; then + git clone community https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + else + git clone --branch ${CIRCLE_BRANCH} https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + fi + - run: *apt-install + - aws-cli/setup: + profile-name: default + - run: + name: update community 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 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: + - DA-ESS-ContentUpdate-latest.tar.gz + update-sources-github: + executor: content-executor + steps: + - attach_workspace: + at: ~/ + - run: *apt-install + - run: + name: checkout repo + command: | + mkdir ~/latest + tar -zxf ~/DA-ESS-ContentUpdate-latest.tar.gz -C ~/latest --strip-components=1 + if [ "${CIRCLE_BRANCH}" == "" ]; then + git clone https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + else + git clone --branch ${CIRCLE_BRANCH} https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + fi + - run: + name: install python dependencies + command: | + cd security-content + rm -rf venv + virtualenv --python=/usr/bin/python3 --clear venv + source venv/bin/activate + pip install -q -r requirements.txt + - run: + name: run doc-gen + command: | + cd security-content + source venv/bin/activate + python bin/doc-gen.py --path . --output docs -v + - run: + name: update github with new docs and package bits + command: | + cd security-content + rm -rf package + mv ~/latest package + # configure git to prep for commit + git config credential.helper 'cache --timeout=120' + git config user.email "research@splunk.com" + git config user.name "research bot" + git config --global push.default simple + git add package/* + git add docs/* + git commit --allow-empty -m "updating docs and package bits [ci skip]" + # Push quietly to prevent showing the token in log + git push https://${GITHUB_TOKEN}@github.com/splunk/security-content.git ${CIRCLE_BRANCH} + - persist_to_workspace: + root: ~/ + paths: + - DA-ESS-ContentUpdate-latest.tar.gz + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + + publish-github-release: + docker: + - image: cibuilds/github:0.10 + steps: + - attach_workspace: + at: ~/ + - run: + name: publish release on github + command: | + cp ~/DA-ESS-ContentUpdate-latest.tar.gz ~/DA-ESS-ContentUpdate-${CIRCLE_TAG}.tar.gz + ghr -t ${GITHUB_TOKEN} -u ${CIRCLE_PROJECT_USERNAME} -r ${CIRCLE_PROJECT_REPONAME} -c ${CIRCLE_SHA1} -delete ${CIRCLE_TAG} ~/DA-ESS-ContentUpdate-${CIRCLE_TAG}.tar.gz + - persist_to_workspace: + root: ~/ + paths: + - DA-ESS-ContentUpdate-latest.tar.gz + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + + attack-range-update: + executor: aws-cli/default + steps: + - attach_workspace: + at: ~/ + - aws-cli/setup: + profile-name: default + - run: + name: sync latest ESCU to the Attack Range S3 bucket for apps + command: | + aws s3 cp ~/DA-ESS-ContentUpdate-latest.tar.gz s3://attack-range-appbinaries/ + # make the file public since it is not by default + aws s3api put-object-acl --bucket attack-range-appbinaries --key DA-ESS-ContentUpdate-latest.tar.gz --acl public-read + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + + master-api-update: + executor: aws-cli/default + steps: + - run: + name: checkout repo + command: | + if [ "${CIRCLE_BRANCH}" == "" ]; then + git clone https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + else + git clone --branch ${CIRCLE_BRANCH} https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + fi + - run: *apt-install + - aws-cli/setup: + profile-name: default + - 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 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" + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + +workflows: + version: 2.1 + test-detections: + jobs: + - test-detections + triggers: + - schedule: + cron: "4 12 * * *" + filters: + branches: + only: + - master + - develop + - automated_detection_testing + validate-and-build: + jobs: + - validate-content: + # build always + filters: + tags: + only: /.*/ + - build-sources: + # build always + requires: + - validate-content + filters: + tags: + only: /.*/ + - build-package: + # build always + requires: + - validate-content + - build-sources + filters: + tags: + only: /.*/ + - run-appinspect: + # build always + requires: + - validate-content + - build-sources + - build-package + filters: + tags: + only: /.*/ + - community-api-update: + # build only on community changes + requires: + - validate-content + - build-sources + - build-package + - run-appinspect + filters: + branches: + only: community + - update-sources-github: + # update package and docs in github if is a tag + requires: + - validate-content + - build-sources + - build-package + - run-appinspect + filters: + tags: + only: /^v.*/ + branches: + ignore: /.*/ + - publish-github-release: + # publish release in github if is a tag + requires: + - validate-content + - build-sources + - build-package + - run-appinspect + - update-sources-github + filters: + tags: + only: /^v.*/ + branches: + ignore: /.*/ + - attack-range-update: + # update the attack range with the latest build + requires: + - validate-content + - build-sources + - build-package + - run-appinspect + - update-sources-github + - publish-github-release + filters: + tags: + only: /^v.*/ + branches: + ignore: /.*/ + - master-api-update: + # update master API from develop if is a tag + requires: + - validate-content + - build-sources + - build-package + - run-appinspect + - update-sources-github + - publish-github-release + - attack-range-update + filters: + tags: + only: /^v.*/ + branches: + ignore: /.*/ diff --git a/bin/jinja2_templates/collections.j2 b/bin/jinja2_templates/collections.j2 new file mode 100644 index 0000000000..f0176a5cbd --- /dev/null +++ b/bin/jinja2_templates/collections.j2 @@ -0,0 +1,13 @@ +############# +# Automatically generated by generator.py in splunk/security-content +# On Date: {{ time }} UTC +# Author: Splunk Security Research +# Contact: research@splunk.com +############# + +{% for lookup in lookups %} +[{{ lookup.name }}] +enforceTypes = false +replicate = false + +{% endfor %} \ No newline at end of file diff --git a/bin/jinja2_templates/savedsearches.j2 b/bin/jinja2_templates/savedsearches.j2 index 68f28ae60f..d714761602 100644 --- a/bin/jinja2_templates/savedsearches.j2 +++ b/bin/jinja2_templates/savedsearches.j2 @@ -94,7 +94,7 @@ search = {{ detection.search }} ### ESCU BASELINES ### {% for baseline in baselines %} -[ESCU - {{ baseline.name }} - Baseline] +[ESCU - {{ baseline.name }}] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -112,6 +112,7 @@ action.escu.data_models = [{{ baseline.data_model | tojson }}] {% else %} action.escu.data_models = [] {% endif %} +cron_schedule = {{ baseline.deployment.scheduling.cron_schedule }} dispatch.earliest_time = {{ baseline.deployment.scheduling.earliest_time }} dispatch.latest_time = {{ baseline.deployment.scheduling.latest_time }} {% if baseline.deployment.scheduling.schedule_window is defined %} diff --git a/bin/jinja2_templates/transforms.j2 b/bin/jinja2_templates/transforms.j2 index da640c650a..797d3ed917 100644 --- a/bin/jinja2_templates/transforms.j2 +++ b/bin/jinja2_templates/transforms.j2 @@ -11,6 +11,7 @@ filename = {{ lookup.filename }} {% else %} collection = {{ lookup.collection }} +external_type = kvstore {% endif %} {% if lookup.default_match is defined %} default_match = {{ lookup.default_match }} @@ -30,5 +31,11 @@ max_matches = {{ lookup.max_matches }} {% if lookup.min_matches is defined %} min_matches = {{ lookup.min_matches }} {% endif %} +{% if lookup.fields_list is defined %} +fields_list = {{ lookup.fields_list }} +{% endif %} +{% if lookup.filter is defined %} +filter = {{ lookup.filter }} +{% endif %} -{% endfor %} +{% endfor %} \ No newline at end of file diff --git a/bin/jinja2_templates/use_case_library.j2 b/bin/jinja2_templates/use_case_library.j2 index 1aa262ca2b..02d026f95c 100644 --- a/bin/jinja2_templates/use_case_library.j2 +++ b/bin/jinja2_templates/use_case_library.j2 @@ -13,7 +13,7 @@ category = {{ story.tags.category[0] }} last_updated = {{ story.date }} version = {{ story.version }} references = {{ story.references | tojson }} -maintainers = {{ story.author | tojson }} +maintainers = [{"company": "{{ story.author_company }}", "email": "-", "name": "{{ story.author_name }}"}] spec_version = 3 searches = {{ story.searches | tojson }} description = {{ story.description }} @@ -29,7 +29,7 @@ narrative = {{ story.narrative }} {% for detection in detections %} [savedsearch://ESCU - {{ detection.name }} - Rule] type = detection -asset_type = {{ detection.asset_type }} +asset_type = {{ detection.tags.asset_type }} confidence = medium explanation = {{ detection.description }} {% if detection.how_to_implement is defined %} @@ -39,7 +39,7 @@ how_to_implement = none {% endif %} annotations = {{ detection.mappings | tojson }} known_false_positives = {{ detection.known_false_positives }} -providing_technologies = none +providing_technologies = [] {% endfor %} ### END DETECTIONS ### diff --git a/deployments/deployment_example_1.yml b/deployments/10_enterprise_security_deployment_configuration.yml similarity index 85% rename from deployments/deployment_example_1.yml rename to deployments/10_enterprise_security_deployment_configuration.yml index 8305cd78a9..69abc643fa 100644 --- a/deployments/deployment_example_1.yml +++ b/deployments/10_enterprise_security_deployment_configuration.yml @@ -4,9 +4,9 @@ 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 + cron_schedule: '0 * * * *' + earliest_time: -70m@m + latest_time: -10m@m schedule_window: auto alert_action: notable: diff --git a/deployments/20_baseline_cache_hourly_updates.yml b/deployments/20_baseline_cache_hourly_updates.yml new file mode 100644 index 0000000000..fdeaf254f2 --- /dev/null +++ b/deployments/20_baseline_cache_hourly_updates.yml @@ -0,0 +1,13 @@ +name: Baseline Cache Hourly Updates +id: 1030c701-2acf-4b1a-9970-46c7145caf2d +date: '2020-06-24' +description: This configuration file applies to all baselines with tag deployments Hourly Cache Updates +author: Bhavin Patel +scheduling: + cron_schedule: '55 * * * *' + earliest_time: -70m@m + latest_time: -10m@m + schedule_window: auto +tags: + deployments: + - Hourly Cache Updates diff --git a/deployments/30_long_running_baseline_searches.yml b/deployments/30_long_running_baseline_searches.yml new file mode 100644 index 0000000000..420ceeee7a --- /dev/null +++ b/deployments/30_long_running_baseline_searches.yml @@ -0,0 +1,13 @@ +name: 90 Day Baseline Searches +id: 6eac9f8b-a35d-4b64-b57f-e5ecde43be6b +date: '2020-06-24' +description: This configuration file applies to all baselines with tag deployments Long Running Baseline +author: Bhavin Patel +scheduling: + cron_schedule: '0 4 * * *' + earliest_time: -90d@d + latest_time: -10m@m + schedule_window: auto +tags: + deployments: + - 90 Day Baseline diff --git a/detections/abnormally_high_aws_instances_launched_by_user.yml b/detections/abnormally_high_aws_instances_launched_by_user.yml index c53af3d951..5a496f8565 100644 --- a/detections/abnormally_high_aws_instances_launched_by_user.yml +++ b/detections/abnormally_high_aws_instances_launched_by_user.yml @@ -28,6 +28,8 @@ tags: - Suspicious AWS EC2 Activities kill_chain_phases: - Actions on Objectives + mitre_attack_id: + - T1078 cis20: - CIS 13 nist: diff --git a/detections/abnormally_high_aws_instances_launched_by_user___mltk.yml b/detections/abnormally_high_aws_instances_launched_by_user___mltk.yml index 41a777f6b4..be65d9b894 100644 --- a/detections/abnormally_high_aws_instances_launched_by_user___mltk.yml +++ b/detections/abnormally_high_aws_instances_launched_by_user___mltk.yml @@ -24,6 +24,8 @@ tags: - Suspicious AWS EC2 Activities kill_chain_phases: - Actions on Objectives + mitre_attack_id: + - T1078 cis20: - CIS 13 nist: diff --git a/detections/abnormally_high_aws_instances_terminated_by_user.yml b/detections/abnormally_high_aws_instances_terminated_by_user.yml index bac36b30da..1da461f261 100644 --- a/detections/abnormally_high_aws_instances_terminated_by_user.yml +++ b/detections/abnormally_high_aws_instances_terminated_by_user.yml @@ -28,6 +28,8 @@ tags: - Suspicious AWS EC2 Activities kill_chain_phases: - Actions on Objectives + mitre_attack_id: + - T1078 cis20: - CIS 13 nist: diff --git a/detections/abnormally_high_aws_instances_terminated_by_user___mltk.yml b/detections/abnormally_high_aws_instances_terminated_by_user___mltk.yml index 94c4b059e3..f98cd8c857 100644 --- a/detections/abnormally_high_aws_instances_terminated_by_user___mltk.yml +++ b/detections/abnormally_high_aws_instances_terminated_by_user___mltk.yml @@ -23,6 +23,8 @@ tags: - Suspicious AWS EC2 Activities kill_chain_phases: - Actions on Objectives + mitre_attack_id: + - T1078 cis20: - CIS 13 nist: diff --git a/detections/access_lsass_memory_for_dump_creation.yml b/detections/access_lsass_memory_for_dump_creation.yml index 0ee95954cb..d1753d4c50 100644 --- a/detections/access_lsass_memory_for_dump_creation.yml +++ b/detections/access_lsass_memory_for_dump_creation.yml @@ -23,7 +23,7 @@ tags: analytics_story: - Credential Dumping mitre_attack_id: - - T1003 + - T1003.001 kill_chain_phases: - Actions on Objectives cis20: diff --git a/detections/attempt_to_add_certificate_to_untrusted_store.yml b/detections/attempt_to_add_certificate_to_untrusted_store.yml index 1a65a56c62..9ed273f5fc 100644 --- a/detections/attempt_to_add_certificate_to_untrusted_store.yml +++ b/detections/attempt_to_add_certificate_to_untrusted_store.yml @@ -14,7 +14,7 @@ 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)` + 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 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 20db8405eb..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 @@ -17,7 +17,7 @@ search: "| tstats `security_content_summariesonly` count min(_time) as firstTime \ (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` " + \ `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 diff --git a/detections/attempted_credential_dump_from_registry_via_reg_exe.yml b/detections/attempted_credential_dump_from_registry_via_reg_exe.yml index 557c69ae1c..12223b5b88 100644 --- a/detections/attempted_credential_dump_from_registry_via_reg_exe.yml +++ b/detections/attempted_credential_dump_from_registry_via_reg_exe.yml @@ -23,7 +23,7 @@ tags: analytics_story: - Credential Dumping mitre_attack_id: - - T1003 + - T1003.002 kill_chain_phases: - Actions on Objectives cis20: diff --git a/detections/aws_cloud_provisioning_from_previously_unseen_city.yml b/detections/aws_cloud_provisioning_from_previously_unseen_city.yml index a128e3fb3b..3957b21928 100644 --- a/detections/aws_cloud_provisioning_from_previously_unseen_city.yml +++ b/detections/aws_cloud_provisioning_from_previously_unseen_city.yml @@ -37,6 +37,8 @@ known_false_positives: "This is a strictly behavioral search, so we define \"fal tags: analytics_story: - AWS Suspicious Provisioning Activities + mitre_attack_id: + - T1535 cis20: - CIS 1 nist: diff --git a/detections/aws_cloud_provisioning_from_previously_unseen_country.yml b/detections/aws_cloud_provisioning_from_previously_unseen_country.yml index 2aba2f4827..af6e8dde55 100644 --- a/detections/aws_cloud_provisioning_from_previously_unseen_country.yml +++ b/detections/aws_cloud_provisioning_from_previously_unseen_country.yml @@ -38,6 +38,8 @@ known_false_positives: "This is a strictly behavioral search, so we define \"fal tags: analytics_story: - AWS Suspicious Provisioning Activities + mitre_attack_id: + - T1535 cis20: - CIS 1 nist: diff --git a/detections/aws_cloud_provisioning_from_previously_unseen_region.yml b/detections/aws_cloud_provisioning_from_previously_unseen_region.yml index 4ae3c4aa77..3ea3a788df 100644 --- a/detections/aws_cloud_provisioning_from_previously_unseen_region.yml +++ b/detections/aws_cloud_provisioning_from_previously_unseen_region.yml @@ -37,6 +37,8 @@ known_false_positives: "This is a strictly behavioral search, so we define \"fal tags: analytics_story: - AWS Suspicious Provisioning Activities + mitre_attack_id: + - T1535 cis20: - CIS 1 nist: diff --git a/detections/aws_cross_account_activity_from_previously_unseen_account.yml b/detections/aws_cross_account_activity_from_previously_unseen_account.yml index 9873b76700..28f49b4fc2 100644 --- a/detections/aws_cross_account_activity_from_previously_unseen_account.yml +++ b/detections/aws_cross_account_activity_from_previously_unseen_account.yml @@ -1,6 +1,6 @@ name: AWS Cross Account Activity From Previously Unseen Account id: 64fbbddf-fabf-4edf-80b3-0cc36ef37727 -version: 2 +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. @@ -15,12 +15,10 @@ 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, strptime(_time,"%Y-%m-%d %H:%M:%S")))) - as firstTime max(eval(coalesce(strptime(_time,"%Y-%m-%d %H:%M:%S"), lastTime))) + | 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, 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 + | 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) @@ -36,6 +34,8 @@ tags: - AWS Cross Account Activity kill_chain_phases: - Actions on Objectives + mitre_attack_id: + - T1078 cis20: - CIS 16 nist: diff --git a/detections/clients_connecting_to_multiple_dns_servers.yml b/detections/clients_connecting_to_multiple_dns_servers.yml index 3de2319de0..b3f12496ec 100644 --- a/detections/clients_connecting_to_multiple_dns_servers.yml +++ b/detections/clients_connecting_to_multiple_dns_servers.yml @@ -27,10 +27,10 @@ known_false_positives: It's possible that an enterprise has more than five DNS s that are configured in a round-robin rotation. Please customize the search, as appropriate. tags: analytics_story: - - Command and Control - - Host Redirection - - Suspicious DNS Traffic - DNS Hijacking + - Command and Control + - Suspicious DNS Traffic + - Host Redirection mitre_attack_id: - T1048 kill_chain_phases: 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 07dbaa29f6..b8353f7744 100644 --- a/detections/cloud_compute_instance_created_by_previously_unseen_user.yml +++ b/detections/cloud_compute_instance_created_by_previously_unseen_user.yml @@ -28,6 +28,8 @@ tags: - Cloud Cryptomining cis20: - CIS 1 + mitre_attack_id: + - T1078 nist: - ID.AM security_domain: endpoint diff --git a/detections/cloud_compute_instance_started_in_previously_unused_region.yml b/detections/cloud_compute_instance_started_in_previously_unused_region.yml index f1e972da8a..af26d3c450 100644 --- a/detections/cloud_compute_instance_started_in_previously_unused_region.yml +++ b/detections/cloud_compute_instance_started_in_previously_unused_region.yml @@ -29,6 +29,8 @@ tags: - Cloud Cryptomining kill_chain_phases: - Actions on Objectives + mitre_attack_id: + - T1535 cis20: - CIS 12 nist: diff --git a/detections/common_ransomware_notes.yml b/detections/common_ransomware_notes.yml index a73dd745c8..4b8b8c9c48 100644 --- a/detections/common_ransomware_notes.yml +++ b/detections/common_ransomware_notes.yml @@ -15,7 +15,7 @@ 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`| + | `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. diff --git a/detections/create_local_admin_accounts_using_net_exe.yml b/detections/create_local_admin_accounts_using_net_exe.yml index 236489d1d2..dd96f19f68 100644 --- a/detections/create_local_admin_accounts_using_net_exe.yml +++ b/detections/create_local_admin_accounts_using_net_exe.yml @@ -14,11 +14,11 @@ 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 (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_accounts_using_net_exe_filter` ' + 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: diff --git a/detections/create_or_delete_windows_shares_using_net_exe.yml b/detections/create_or_delete_windows_shares_using_net_exe.yml index 9eb576fe3a..5e66d89735 100644 --- a/detections/create_or_delete_windows_shares_using_net_exe.yml +++ b/detections/create_or_delete_windows_shares_using_net_exe.yml @@ -19,8 +19,7 @@ search: '| tstats `security_content_summariesonly` count values(Processes.user) 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_using_net_exe_filter` ' + `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: diff --git a/detections/create_remote_thread_into_lsass.yml b/detections/create_remote_thread_into_lsass.yml index 380b6d6eb5..a39a187aad 100644 --- a/detections/create_remote_thread_into_lsass.yml +++ b/detections/create_remote_thread_into_lsass.yml @@ -16,14 +16,14 @@ 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`' + | `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 + - T1003.001 kill_chain_phases: - Actions on Objectives cis20: diff --git a/detections/creation_of_shadow_copy.yml b/detections/creation_of_shadow_copy.yml index cf3cf3bd5c..eafeb29b42 100644 --- a/detections/creation_of_shadow_copy.yml +++ b/detections/creation_of_shadow_copy.yml @@ -26,7 +26,7 @@ tags: analytics_story: - Credential Dumping mitre_attack_id: - - T1003 + - T1003.003 kill_chain_phases: - Actions on Objectives cis20: 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 99dd07169e..68d9a3ed95 100644 --- a/detections/creation_of_shadow_copy_with_wmic_and_powershell.yml +++ b/detections/creation_of_shadow_copy_with_wmic_and_powershell.yml @@ -4,26 +4,21 @@ 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 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` ' +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.process_name=wmic* OR Processes.process_name=powershell* Processes.process=*shadowcopy* Processes.process=*create* + by Processes.user Processes.process_name Processes.process Processes.dest + | `drop_dm_object_name(Processes)` | `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 + - T1003.003 kill_chain_phases: - Actions on Objectives cis20: diff --git a/detections/credential_dumping_via_copy_command_from_shadow_copy.yml b/detections/credential_dumping_via_copy_command_from_shadow_copy.yml index 0031a9b18a..7cdb5b8c2a 100644 --- a/detections/credential_dumping_via_copy_command_from_shadow_copy.yml +++ b/detections/credential_dumping_via_copy_command_from_shadow_copy.yml @@ -24,7 +24,7 @@ tags: analytics_story: - Credential Dumping mitre_attack_id: - - T1003 + - T1003.003 kill_chain_phases: - Actions on Objectives cis20: diff --git a/detections/credential_dumping_via_symlink_to_shadow_copy.yml b/detections/credential_dumping_via_symlink_to_shadow_copy.yml index 2be920fab2..b5bd6759d2 100644 --- a/detections/credential_dumping_via_symlink_to_shadow_copy.yml +++ b/detections/credential_dumping_via_symlink_to_shadow_copy.yml @@ -22,7 +22,7 @@ tags: analytics_story: - Credential Dumping mitre_attack_id: - - T1003 + - T1003.003 kill_chain_phases: - Actions on Objectives cis20: diff --git a/detections/deleting_shadow_copies.yml b/detections/deleting_shadow_copies.yml index c04ba7e5a8..62f95fe846 100644 --- a/detections/deleting_shadow_copies.yml +++ b/detections/deleting_shadow_copies.yml @@ -24,8 +24,8 @@ known_false_positives: vssadmin.exe and wmic.exe are standard applications shipp delete old backup copies, although this is typically rare. tags: analytics_story: - - SamSam Ransomware - Windows Log Manipulation + - SamSam Ransomware - Ransomware kill_chain_phases: - Actions on Objectives diff --git a/detections/detect_attackers_scanning_for_vulnerable_jboss_servers.yml b/detections/detect_attackers_scanning_for_vulnerable_jboss_servers.yml index 7b7dfec741..9cc94a66ce 100644 --- a/detections/detect_attackers_scanning_for_vulnerable_jboss_servers.yml +++ b/detections/detect_attackers_scanning_for_vulnerable_jboss_servers.yml @@ -19,8 +19,8 @@ known_false_positives: It's possible for legitimate HTTP requests to be made to containing the suspicious paths. tags: analytics_story: - - SamSam Ransomware - JBoss Vulnerability + - SamSam Ransomware mitre_attack_id: - T1082 kill_chain_phases: diff --git a/detections/detect_aws_api_activities_from_unapproved_accounts.yml b/detections/detect_aws_api_activities_from_unapproved_accounts.yml index 6539c00e6e..6b5d0f1902 100644 --- a/detections/detect_aws_api_activities_from_unapproved_accounts.yml +++ b/detections/detect_aws_api_activities_from_unapproved_accounts.yml @@ -51,6 +51,8 @@ tags: - Actions on Objectives cis20: - CIS 16 + mitre_attack_id: + - T1078 nist: - DE.DP - DE.CM 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 index a3797cc46e..a6161e1748 100644 --- a/detections/detect_aws_console_login_by_user_from_new_city.yml +++ b/detections/detect_aws_console_login_by_user_from_new_city.yml @@ -30,8 +30,11 @@ known_false_positives: When a legitimate new user logins for the first time, thi tags: analytics_story: - Suspicious AWS Login Activities + - Suspicious Cloud Authentication Activities kill_chain_phases: - Actions on Objectives + mitre_attack_id: + - T1535 cis20: - CIS 16 nist: 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 index 60fccc3b0a..b4c850f5ec 100644 --- a/detections/detect_aws_console_login_by_user_from_new_country.yml +++ b/detections/detect_aws_console_login_by_user_from_new_country.yml @@ -30,8 +30,11 @@ known_false_positives: When a legitimate new user logins for the first time, thi tags: analytics_story: - Suspicious AWS Login Activities + - Suspicious Cloud Authentication Activities kill_chain_phases: - Actions on Objectives + mitre_attack_id: + - T1535 cis20: - CIS 16 nist: 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 index 47ac42c4f2..7acded3d55 100644 --- a/detections/detect_aws_console_login_by_user_from_new_region.yml +++ b/detections/detect_aws_console_login_by_user_from_new_region.yml @@ -30,8 +30,11 @@ known_false_positives: When a legitimate new user logins for the first time, thi tags: analytics_story: - Suspicious AWS Login Activities + - Suspicious Cloud Authentication Activities kill_chain_phases: - Actions on Objectives + mitre_attack_id: + - T1535 cis20: - CIS 16 nist: diff --git a/detections/detect_credential_dumping_through_LSASS_access.yml b/detections/detect_credential_dumping_through_lsass_access.yml similarity index 99% rename from detections/detect_credential_dumping_through_LSASS_access.yml rename to detections/detect_credential_dumping_through_lsass_access.yml index bcff7da23a..3df17ce650 100644 --- a/detections/detect_credential_dumping_through_LSASS_access.yml +++ b/detections/detect_credential_dumping_through_lsass_access.yml @@ -26,7 +26,7 @@ tags: analytics_story: - Credential Dumping mitre_attack_id: - - T1003 + - T1003.001 kill_chain_phases: - Actions on Objectives cis20: diff --git a/detections/detect_hosts_connecting_to_dynamic_domain_providers.yml b/detections/detect_hosts_connecting_to_dynamic_domain_providers.yml index 71f1c02a11..8c3fcfa8fb 100644 --- a/detections/detect_hosts_connecting_to_dynamic_domain_providers.yml +++ b/detections/detect_hosts_connecting_to_dynamic_domain_providers.yml @@ -44,12 +44,12 @@ known_false_positives: Some users and applications may leverage Dynamic DNS to r however this activity must be verified. tags: analytics_story: + - Data Protection - Prohibited Traffic Allowed or Protocol Mismatch - - Suspicious DNS Traffic - DNS Hijacking + - Suspicious DNS Traffic - Dynamic DNS - Command and Control - - Data Protection kill_chain_phases: - Command and Control - Actions on Objectives diff --git a/detections/detect_long_dns_txt_record_response.yml b/detections/detect_long_dns_txt_record_response.yml index b335fa7f35..12a52379a4 100644 --- a/detections/detect_long_dns_txt_record_response.yml +++ b/detections/detect_long_dns_txt_record_response.yml @@ -28,8 +28,8 @@ known_false_positives: It's possible that legitimate TXT record responses can be to help mitigate false positives. tags: analytics_story: - - Command and Control - Suspicious DNS Traffic + - Command and Control mitre_attack_id: - T1043 kill_chain_phases: diff --git a/detections/detect_malicious_requests_to_exploit_jboss_servers.yml b/detections/detect_malicious_requests_to_exploit_jboss_servers.yml index 49be7121db..ba130244e6 100644 --- a/detections/detect_malicious_requests_to_exploit_jboss_servers.yml +++ b/detections/detect_malicious_requests_to_exploit_jboss_servers.yml @@ -20,8 +20,8 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime known_false_positives: No known false positives for this detection. tags: analytics_story: - - SamSam Ransomware - JBoss Vulnerability + - SamSam Ransomware kill_chain_phases: - Delivery cis20: diff --git a/detections/detect_new_api_calls_from_user_roles.yml b/detections/detect_new_api_calls_from_user_roles.yml index ccba53241e..c0a1bf5067 100644 --- a/detections/detect_new_api_calls_from_user_roles.yml +++ b/detections/detect_new_api_calls_from_user_roles.yml @@ -28,6 +28,9 @@ known_false_positives: It is possible that there are legitimate user roles makin tags: analytics_story: - AWS User Monitoring + mitre_attack_id: + - T1078 + - T1098 cis20: - CIS 1 nist: diff --git a/detections/detect_new_local_admin_account.yml b/detections/detect_new_local_admin_account.yml index 4d8279cc53..190f8fab0e 100644 --- a/detections/detect_new_local_admin_account.yml +++ b/detections/detect_new_local_admin_account.yml @@ -1,41 +1,18 @@ name: Detect New Local Admin account id: b25f6f62-0712-43c1-b203-083231ffd97d -version: 1 -date: '2019-02-28' +version: 2 +date: '2020-07-08' 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`' +search: '`wineventlog_security` EventID=4720 OR (EventID=4732 Group_Name=Administrators) + | transaction MemberSid connected=false maxspan=180m + | rename MemberSid as user + | stats count min(_time) as firstTime max(_time) as lastTime by user dest + | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + | `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 @@ -44,7 +21,7 @@ tags: analytics_story: - DHS Report TA18-074A mitre_attack_id: - - T1078 + - T1136.001 kill_chain_phases: - Actions on Objectives - Command and Control diff --git a/detections/detect_new_open_s3_buckets.yml b/detections/detect_new_open_s3_buckets.yml index 889947bac6..bce49e3cf9 100644 --- a/detections/detect_new_open_s3_buckets.yml +++ b/detections/detect_new_open_s3_buckets.yml @@ -25,6 +25,8 @@ tags: - Suspicious AWS S3 Activities kill_chain_phases: - Actions on Objectives + mitre_attack_id: + - T1530 cis20: - CIS 13 nist: diff --git a/detections/detect_new_user_aws_console_login.yml b/detections/detect_new_user_aws_console_login.yml index 18071951e4..33f7c81de0 100644 --- a/detections/detect_new_user_aws_console_login.yml +++ b/detections/detect_new_user_aws_console_login.yml @@ -29,6 +29,8 @@ tags: - Suspicious AWS Login Activities kill_chain_phases: - Actions on Objectives + mitre_attack_id: + - T1078 cis20: - CIS 16 nist: diff --git a/detections/detect_new_user_aws_console_login___dm.yml b/detections/detect_new_user_aws_console_login___dm.yml new file mode 100644 index 0000000000..fa1782de9e --- /dev/null +++ b/detections/detect_new_user_aws_console_login___dm.yml @@ -0,0 +1,33 @@ +name: Detect new user AWS Console Login - DM +id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 +version: 1 +date: '2020-05-28' +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 and configure the Splunk Add-on for AWS (version 5.1.0 or later) + and Enterprise Security 6.2, which contains the required updates to the Authentication data model + for cloud use cases. 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: Rico Valdez, Splunk +search: "| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user | `drop_dm_object_name(Authentication)` | 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)`| `detect_new_user_aws_console_login___dm_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 Cloud Authentication 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_path_interception_by_creation_of_program_exe.yml b/detections/detect_path_interception_by_creation_of_program_exe.yml index 39dcbe18b9..6d652ece41 100644 --- a/detections/detect_path_interception_by_creation_of_program_exe.yml +++ b/detections/detect_path_interception_by_creation_of_program_exe.yml @@ -1,28 +1,24 @@ 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. +version: 3 +date: '2020-07-03' +description: 'The detection Detect Path Interception By Creation Of program exe is detecting the abuse of + unquoted service paths, which is a popular technique for privilege escalation. ' 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.' +references: + - https://medium.com/@SumitVerma101/windows-privilege-escalation-part-1-unquoted-service-path-c7a011a8d8ae +author: Patrick Bareiss, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from + datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe by Processes.user Processes.process_name + Processes.process Processes.dest | `drop_dm_object_name(Processes)` | rex field=process "^.*\\\\(?.*\.(?:exe|bat|com|ps1))" + | eval process_name = lower(process_name) | eval service_process = lower(service_process)| where process_name != service_process + | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_path_interception_by_creation_of_program_exe_filter`' +known_false_positives: unknown tags: analytics_story: - Windows Persistence Techniques + mitre_attack_id: + - T1054.009 kill_chain_phases: - Actions on Objectives cis20: @@ -31,4 +27,4 @@ tags: - PR.PT - DE.CM security_domain: endpoint - asset_type: '' + asset_type: Endpoint diff --git a/detections/detect_prohibited_applications_spawning_cmd_exe.yml b/detections/detect_prohibited_applications_spawning_cmd_exe.yml index 94a4c9cb39..9f73b49c61 100644 --- a/detections/detect_prohibited_applications_spawning_cmd_exe.yml +++ b/detections/detect_prohibited_applications_spawning_cmd_exe.yml @@ -24,6 +24,7 @@ tags: analytics_story: - Suspicious Command-Line Executions - Suspicious MSHTA Activity + - Suspicious Zoom Child Processes mitre_attack_id: - T1059 kill_chain_phases: diff --git a/detections/detect_s3_access_from_a_new_ip.yml b/detections/detect_s3_access_from_a_new_ip.yml index 0230e522c9..9d9367c70c 100644 --- a/detections/detect_s3_access_from_a_new_ip.yml +++ b/detections/detect_s3_access_from_a_new_ip.yml @@ -28,6 +28,8 @@ tags: - Suspicious AWS S3 Activities kill_chain_phases: - Actions on Objectives + mitre_attack_id: + - T1530 cis20: - CIS 13 - CIS 14 diff --git a/detections/detect_spike_in_aws_api_activity.yml b/detections/detect_spike_in_aws_api_activity.yml index 297e860092..aaa5ccb783 100644 --- a/detections/detect_spike_in_aws_api_activity.yml +++ b/detections/detect_spike_in_aws_api_activity.yml @@ -54,6 +54,8 @@ tags: - AWS User Monitoring kill_chain_phases: - Actions on Objectives + mitre_attack_id: + - T1078 cis20: - CIS 16 nist: 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 index e06ea84b01..fcdd2fdaa9 100644 --- a/detections/detect_spike_in_blocked_outbound_traffic_from_your_aws.yml +++ b/detections/detect_spike_in_blocked_outbound_traffic_from_your_aws.yml @@ -41,9 +41,9 @@ known_false_positives: The false-positive rate may vary based on the values of`d of blocked outbound connections. tags: analytics_story: - - Command and Control - AWS Network ACL Activity - Suspicious AWS Traffic + - Command and Control kill_chain_phases: - Actions on Objectives - Command and Control diff --git a/detections/detect_spike_in_s3_bucket_deletion.yml b/detections/detect_spike_in_s3_bucket_deletion.yml index ddfd7e1b94..6bed383c97 100644 --- a/detections/detect_spike_in_s3_bucket_deletion.yml +++ b/detections/detect_spike_in_s3_bucket_deletion.yml @@ -36,6 +36,8 @@ known_false_positives: Based on the values of`dataPointThreshold` and `deviation tags: analytics_story: - Suspicious AWS S3 Activities + mitre_attack_id: + - T1530 kill_chain_phases: - Actions on Objectives cis20: diff --git a/detections/detect_spike_in_security_group_activity.yml b/detections/detect_spike_in_security_group_activity.yml index 92c2cc49ae..ea9f1d25dd 100644 --- a/detections/detect_spike_in_security_group_activity.yml +++ b/detections/detect_spike_in_security_group_activity.yml @@ -39,6 +39,8 @@ tags: - AWS User Monitoring kill_chain_phases: - Actions on Objectives + mitre_attack_id: + - T1078 cis20: - CIS 16 nist: 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 index 37fac4086e..d261e24136 100644 --- a/detections/detect_use_of_cmd_exe_to_launch_script_interpreters.yml +++ b/detections/detect_use_of_cmd_exe_to_launch_script_interpreters.yml @@ -22,8 +22,8 @@ search: '| tstats `security_content_summariesonly` count values(Processes.proces known_false_positives: Some legitimate applications may exhibit this behavior. tags: analytics_story: - - Suspicious Command-Line Executions - 'Emotet Malware DHS Report TA18-201A ' + - Suspicious Command-Line Executions mitre_attack_id: - T1059 kill_chain_phases: diff --git a/detections/detection_of_dns_tunnels.yml b/detections/detection_of_dns_tunnels.yml index b5cb9d2b95..2027a04314 100644 --- a/detections/detection_of_dns_tunnels.yml +++ b/detections/detection_of_dns_tunnels.yml @@ -39,9 +39,9 @@ known_false_positives: It's possible that normal DNS traffic will exhibit this b can also be modified to better suit your environment. tags: analytics_story: - - Command and Control - Data Protection - Suspicious DNS Traffic + - Command and Control mitre_attack_id: - T1043 kill_chain_phases: diff --git a/detections/disabling_remote_user_account_control.yml b/detections/disabling_remote_user_account_control.yml index 878a50c013..ecb9b0b969 100644 --- a/detections/disabling_remote_user_account_control.yml +++ b/detections/disabling_remote_user_account_control.yml @@ -21,8 +21,8 @@ known_false_positives: This registry key may be modified via administrators to i a change in system policy. This type of change should be a very rare occurrence. tags: analytics_story: - - Suspicious Windows Registry Activities - Windows Defense Evasion Tactics + - Suspicious Windows Registry Activities mitre_attack_id: - T1112 kill_chain_phases: diff --git a/detections/dns_query_length_outliers___mltk.yml b/detections/dns_query_length_outliers___mltk.yml index 960bc2a820..6f1bab7aa8 100644 --- a/detections/dns_query_length_outliers___mltk.yml +++ b/detections/dns_query_length_outliers___mltk.yml @@ -47,9 +47,9 @@ known_false_positives: If you are seeing more results than desired, you may cons the support search to re-build the ML model on the latest data. tags: analytics_story: - - Command and Control - Hidden Cobra Malware - Suspicious DNS Traffic + - Command and Control mitre_attack_id: - T1043 kill_chain_phases: diff --git a/detections/dns_query_length_with_high_standard_deviation.yml b/detections/dns_query_length_with_high_standard_deviation.yml index 1d612a5ac3..1d658defc6 100644 --- a/detections/dns_query_length_with_high_standard_deviation.yml +++ b/detections/dns_query_length_with_high_standard_deviation.yml @@ -18,9 +18,9 @@ search: '| tstats `security_content_summariesonly` count from datamodel=Network_ known_false_positives: It's possible there can be long domain names that are legitimate. tags: analytics_story: - - Command and Control - Hidden Cobra Malware - Suspicious DNS Traffic + - Command and Control mitre_attack_id: - T1043 kill_chain_phases: diff --git a/detections/dns_query_requests_resolved_by_unauthorized_dns_servers.yml b/detections/dns_query_requests_resolved_by_unauthorized_dns_servers.yml index 1ea4c64a62..67fba352f1 100644 --- a/detections/dns_query_requests_resolved_by_unauthorized_dns_servers.yml +++ b/detections/dns_query_requests_resolved_by_unauthorized_dns_servers.yml @@ -18,10 +18,10 @@ known_false_positives: Legitimate DNS activity can be detected in this search. I verify and update the list of authorized DNS servers as appropriate. tags: analytics_story: - - Command and Control - - Host Redirection - - Suspicious DNS Traffic - DNS Hijacking + - Command and Control + - Suspicious DNS Traffic + - Host Redirection kill_chain_phases: - Command and Control cis20: diff --git a/detections/dump_lsass_via_comsvcs_dll.yml b/detections/dump_lsass_via_comsvcs_dll.yml index 4a1800d7b3..ed924c62fa 100644 --- a/detections/dump_lsass_via_comsvcs_dll.yml +++ b/detections/dump_lsass_via_comsvcs_dll.yml @@ -22,7 +22,7 @@ tags: analytics_story: - Credential Dumping mitre_attack_id: - - T1003 + - T1003.001 kill_chain_phases: - Actions on Objectives cis20: diff --git a/detections/ec2_instance_modified_with_previously_unseen_user.yml b/detections/ec2_instance_modified_with_previously_unseen_user.yml index 88e2e4f956..f6c67ea26c 100644 --- a/detections/ec2_instance_modified_with_previously_unseen_user.yml +++ b/detections/ec2_instance_modified_with_previously_unseen_user.yml @@ -26,6 +26,8 @@ known_false_positives: It's possible that a new user will start to modify EC2 in tags: analytics_story: - Unusual AWS EC2 Modifications + mitre_attack_id: + - T1078 cis20: - CIS 1 nist: diff --git a/detections/ec2_instance_started_in_previously_unseen_region.yml b/detections/ec2_instance_started_in_previously_unseen_region.yml index ab52ff8feb..e2a2d6f12d 100644 --- a/detections/ec2_instance_started_in_previously_unseen_region.yml +++ b/detections/ec2_instance_started_in_previously_unseen_region.yml @@ -27,6 +27,9 @@ tags: - Suspicious AWS EC2 Activities kill_chain_phases: - Actions on Objectives + mitre_attack_id: + - T1078 + - T1535 cis20: - CIS 12 nist: diff --git a/detections/ec2_instance_started_with_previously_unseen_user.yml b/detections/ec2_instance_started_with_previously_unseen_user.yml index 2eb9eec2d6..b2f4bdce6a 100644 --- a/detections/ec2_instance_started_with_previously_unseen_user.yml +++ b/detections/ec2_instance_started_with_previously_unseen_user.yml @@ -27,6 +27,8 @@ tags: analytics_story: - AWS Cryptomining - Suspicious AWS EC2 Activities + mitre_attack_id: + - T1078 cis20: - CIS 1 nist: diff --git a/detections/email_attachments_with_lots_of_spaces.yml b/detections/email_attachments_with_lots_of_spaces.yml index 90ffc1cc0d..cf550ea395 100644 --- a/detections/email_attachments_with_lots_of_spaces.yml +++ b/detections/email_attachments_with_lots_of_spaces.yml @@ -29,8 +29,8 @@ search: '| tstats `security_content_summariesonly` count values(All_Email.recipi known_false_positives: None at this time tags: analytics_story: - - Suspicious Emails - 'Emotet Malware DHS Report TA18-201A ' + - Suspicious Emails kill_chain_phases: - Delivery cis20: diff --git a/detections/excessive_dns_failures.yml b/detections/excessive_dns_failures.yml index 6289c61f59..d2e6d34058 100644 --- a/detections/excessive_dns_failures.yml +++ b/detections/excessive_dns_failures.yml @@ -21,8 +21,8 @@ known_false_positives: It is possible legitimate traffic can trigger this rule. to better suit your environment. tags: analytics_story: - - Command and Control - Suspicious DNS Traffic + - Command and Control mitre_attack_id: - T1048 - T1043 diff --git a/detections/first_time_seen_child_process_of_zoom.yml b/detections/first_time_seen_child_process_of_zoom.yml new file mode 100644 index 0000000000..26cf530e42 --- /dev/null +++ b/detections/first_time_seen_child_process_of_zoom.yml @@ -0,0 +1,38 @@ +name: First Time Seen Child Process of Zoom +id: e91bd102-d630-4e76-ab73-7e3ba22c5961 +version: 1 +date: '2020-05-20' +description: This search looks for child processes spawned by zoom.exe or zoom.us 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 should run the baseline search `Previously Seen Zoom Child Processes - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Zoom Child Processes - Update` to keep this table up to date and to age out old child processes. Please update the `previously_seen_zoom_child_processes_window` macro to adjust the time window. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` min(_time) as firstTime + values(Processes.parent_process_name) as parent_process_name + values(Processes.parent_process_id) as parent_process_id + values(Processes.process_name) as process_name values(Processes.process) as process + from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) + by Processes.process_id Processes.dest + | `drop_dm_object_name(Processes)` + | lookup zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen + | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") + | `security_content_ctime(firstTime)` + | table firstTime dest, process_id, process_name, parent_process_id, parent_process_name |`first_time_seen_child_process_of_zoom_filter`' +known_false_positives: A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken. +tags: + analytics_story: + - Suspicious Zoom Child Processes + mitre_attack_id: + - T1068 + kill_chain_phases: + - 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_command_line_argument.yml b/detections/first_time_seen_command_line_argument.yml index b15dcdd2a9..4e65aa9096 100644 --- a/detections/first_time_seen_command_line_argument.yml +++ b/detections/first_time_seen_command_line_argument.yml @@ -34,11 +34,11 @@ known_false_positives: Legitimate programs can also use command-line arguments t macro to exclude legitimate parent_process_name tags: analytics_story: - - Suspicious Command-Line Executions - - Hidden Cobra Malware - DHS Report TA18-074A - - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Suspicious Command-Line Executions - Orangeworm Attack Group + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Hidden Cobra Malware mitre_attack_id: - T1064 - T1059 diff --git a/detections/first_time_seen_running_windows_service.yml b/detections/first_time_seen_running_windows_service.yml index 7700039828..7cfd171ffc 100644 --- a/detections/first_time_seen_running_windows_service.yml +++ b/detections/first_time_seen_running_windows_service.yml @@ -1,28 +1,27 @@ 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. +version: 3 +date: '2020-06-15' +description: This search looks for the first and last time a Windows service is seen running + in your environment. This table is then cached. 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. + you must be ingesting your Windows system event logs in order for this search + to execute successfully. You should run the baseline search + `Previously Seen Running Windows Services - Initial` to build the initial table of child + processes and hostnames for this search to work. You should also schedule at the same interval + as this search the second baseline search `Previously Seen Running Windows Services - Update` to + keep this table up to date and to age out old Windows Services. Please update the + `previously_seen_windows_service_window` macro to adjust the time window. + Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 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`' +search: '`wineventlog_system` EventCode=7036 | + rex field=Message "The (?[-\(\)\s\w]+) service entered the (?\w+) state" | + where state="running" | + lookup previously_seen_running_windows_services service as service OUTPUT firstTimeSeen | + where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_windows_service_window`") | + table _time dest service | `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. tags: diff --git a/detections/gcp_gcr_container_uploaded.yml b/detections/gcp_gcr_container_uploaded.yml index 40c531d147..2c4b21a42d 100644 --- a/detections/gcp_gcr_container_uploaded.yml +++ b/detections/gcp_gcr_container_uploaded.yml @@ -25,3 +25,5 @@ tags: - Container Implantation Monitoring and Investigation security_domain: threat asset_type: GCP GCR Container + mitre_attack_id: + - T1525 diff --git a/detections/hiding_files_and_directories_with_attrib_exe.yml b/detections/hiding_files_and_directories_with_attrib_exe.yml index 3cbb39cf58..84db36db2f 100644 --- a/detections/hiding_files_and_directories_with_attrib_exe.yml +++ b/detections/hiding_files_and_directories_with_attrib_exe.yml @@ -17,14 +17,14 @@ 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 - | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| + 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 Persistence Techniques - Windows Defense Evasion Tactics + - Windows Persistence Techniques kill_chain_phases: - Actions on Objectives cis20: diff --git a/detections/kerberoasting_spn_request_with_rc4_encryption.yml b/detections/kerberoasting_spn_request_with_rc4_encryption.yml new file mode 100644 index 0000000000..826655c5b9 --- /dev/null +++ b/detections/kerberoasting_spn_request_with_rc4_encryption.yml @@ -0,0 +1,31 @@ +name: Kerberoasting spn request with RC4 encryption +id: 5cc67381-44fa-4111-8a37-7a230943f027 +version: 1 +date: '2020-06-11' +description: This search detects a potential kerberoasting attack via service principal name requests +how_to_implement: You must be ingesting endpoint data that tracks process activity, and include the windows security event logs that contain kerberos +type: ESCU +references: + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1208/T1208.md + - https://www.trimarcsecurity.com/post/trimarcresearch-detecting-kerberoasting-activity +author: Jose Hernandez, Splunk +search: '`wineventlog_security` EventID=4769 TicketOptions=0x40810000 TicketEncryptionType=0x17 +| stats count min(_time) as firstTime max(_time) as lastTime values(ServiceName) values(TargetUserName) values(user) by TargetDomainName +| `security_content_ctime(lastTime)` +| `security_content_ctime(firstTime)` +| `kerberoasting_spn_request_with_rc4_encryption_filter`' +known_false_positives: Older systems that support kerberos RC4 by default NetApp may generate false positives +tags: + analytics_story: + - Lateral Movement + mitre_attack_id: + - T1208 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + - CIS 16 + nist: + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/kubernetes_aws_detect_most_active_service_accounts_by_pod.yml b/detections/kubernetes_aws_detect_most_active_service_accounts_by_pod.yml index 7861205746..70c6e10b56 100644 --- a/detections/kubernetes_aws_detect_most_active_service_accounts_by_pod.yml +++ b/detections/kubernetes_aws_detect_most_active_service_accounts_by_pod.yml @@ -4,9 +4,9 @@ description: "This search provides information on Kubernetes service accounts,ac how_to_implement: "You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs" id: 5b30b25d-7d32-42d8-95ca-64dfcd9076e6 known_false_positives: "Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness." -name: "AWs EKS Kubernetes detect most active service accounts by pod." +name: "Kubernetes AWS detect most active service accounts by pod" references: [] -search: sourcetype=aws:cloudwatchlogs:eks user.groups{}=system:serviceaccounts objectRef.resource=pods | table sourceIPs{} user.username userAgent verb annotations.authorization.k8s.io/decision | top sourceIPs{} user.username verb annotations.authorization.k8s.io/decision |`kubernetes_aws_detect_most_active_service_accounts_by_pod` +search: "`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts objectRef.resource=pods | table sourceIPs{} user.username userAgent verb annotations.authorization.k8s.io/decision | top sourceIPs{} user.username verb annotations.authorization.k8s.io/decision |`kubernetes_aws_detect_most_active_service_accounts_by_pod_filter`" tags: analytics_story: - "Kubernetes Sensitive Role Activity" diff --git a/detections/kubernetes_aws_detect_RBAC_authorizations_by_account.yml b/detections/kubernetes_aws_detect_rbac_authorizations_by_account.yml similarity index 65% rename from detections/kubernetes_aws_detect_RBAC_authorizations_by_account.yml rename to detections/kubernetes_aws_detect_rbac_authorizations_by_account.yml index 1e6799b4f9..4e924129c4 100644 --- a/detections/kubernetes_aws_detect_RBAC_authorizations_by_account.yml +++ b/detections/kubernetes_aws_detect_rbac_authorizations_by_account.yml @@ -4,9 +4,9 @@ description: "This search provides information on Kubernetes RBAC authorizations how_to_implement: "You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs" id: de7264ed-3ed9-4fef-bb01-6eefc87cefe8 known_false_positives: "Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted." -name: "AWS EKS Kubernetes RBAC authorizations by account" +name: "Kubernetes AWS detect RBAC authorization by account" references: [] -search: sourcetype="aws:cloudwatchlogs:eks" annotations.authorization.k8s.io/reason=* | table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason | stats count by user.username annotations.authorization.k8s.io/reason | rare user.username annotations.authorization.k8s.io/reason |`kubernetes_aws_detect_RBAC_authorization_by_account` +search: "`aws_cloudwatchlogs_eks` annotations.authorization.k8s.io/reason=* | table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason | stats count by user.username annotations.authorization.k8s.io/reason | rare user.username annotations.authorization.k8s.io/reason |`kubernetes_aws_detect_rbac_authorization_by_account_filter`" tags: analytics_story: - "Kubernetes Sensitive Role Activity" diff --git a/detections/kubernetes_aws_detect_sensitive_object_access.yml b/detections/kubernetes_aws_detect_sensitive_object_access.yml index 0b4e6f1e18..fec9283df6 100644 --- a/detections/kubernetes_aws_detect_sensitive_object_access.yml +++ b/detections/kubernetes_aws_detect_sensitive_object_access.yml @@ -4,9 +4,9 @@ description: "This search provides information on Kubernetes accounts accessing how_to_implement: "You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs." id: 7f227943-2196-4d4d-8d6a-ac8cb308e61c known_false_positives: "Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection." -name: "AWS EKS Kubernetes cluster sensitive object access detection" +name: "AWS EKS Kubernetes cluster sensitive object access" references: [] -search: sourcetype="aws:cloudwatchlogs:eks" objectRef.resource=secrets OR configmaps sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 |table sourceIPs{} user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason |dedup user.username user.groups{} |`kubernetes_aws_detect_sensitive_object_access`" +search: "`aws_cloudwatchlogs_eks` objectRef.resource=secrets OR configmaps sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 |table sourceIPs{} user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason |dedup user.username user.groups{} |`aws_eks_kubernetes_cluster_sensitive_object_access_filter`" tags: analytics_story: - "Kubernetes Sensitive Object Access Activity" diff --git a/detections/kubernetes_aws_detect_sensitive_role_access.yml b/detections/kubernetes_aws_detect_sensitive_role_access.yml index 4eb1fa7d69..f961ab7ed4 100644 --- a/detections/kubernetes_aws_detect_sensitive_role_access.yml +++ b/detections/kubernetes_aws_detect_sensitive_role_access.yml @@ -4,9 +4,9 @@ description: "This search provides information on Kubernetes accounts accessing how_to_implement: "You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs." id: b6013a7b-85e0-4a45-b051-10b252d69569 known_false_positives: "Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. " -name: "AWS EKS Kubernetes cluster sensitive role access detection" +name: "Kubernetes AWS detect sensitive role access" references: [] -search: sourcetype="aws:cloudwatchlogs:eks" objectRef.resource=clusterroles OR clusterrolebindings sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 | table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason | dedup user.username user.groups{} |`kubernetes_aws_detect_sensitive_role_access` +search: "`aws_cloudwatchlogs_eks` objectRef.resource=clusterroles OR clusterrolebindings sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 | table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason | dedup user.username user.groups{} |`kubernetes_aws_detect_sensitive_role_access_filter`" tags: analytics_story: - "Kubernetes Sensitive Role Activity" diff --git a/detections/kubernetes_aws_detect_service_accounts_forbidden_failure_access.yml b/detections/kubernetes_aws_detect_service_accounts_forbidden_failure_access.yml index 6372a61134..512a41b4b6 100644 --- a/detections/kubernetes_aws_detect_service_accounts_forbidden_failure_access.yml +++ b/detections/kubernetes_aws_detect_service_accounts_forbidden_failure_access.yml @@ -4,9 +4,9 @@ description: "This search provides information on Kubernetes service accounts wi how_to_implement: "You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs." id: a6959c57-fa8f-4277-bb86-7c32fba579d5 known_false_positives: "This search can give false positives as there might be inherent issues with authentications and permissions at cluster." -name: "AWS EKS Kubernetes cluster sensitive object access detection" +name: "Kubernetes AWS detect service accounts forbidden failure access" references: [] -search: sourcetype=aws:cloudwatchlogs:eks user.groups{}=system:serviceaccounts responseStatus.status = Failure | table sourceIPs{} src_user userAgent verb responseStatus.status requestURI | `kubernetes_aws_detect_service_accounts_forbidden_failure_access` +search: "`aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts responseStatus.status = Failure | table sourceIPs{} src_user userAgent verb responseStatus.status requestURI | `kubernetes_aws_detect_service_accounts_forbidden_failure_access_filter`" tags: analytics_story: - "Kubernetes Sensitive Object Access Activity" diff --git a/detections/kubernetes_aws_detect_suspicious_kubectl_calls.yml b/detections/kubernetes_aws_detect_suspicious_kubectl_calls.yml index 13d864553c..cce8135a08 100644 --- a/detections/kubernetes_aws_detect_suspicious_kubectl_calls.yml +++ b/detections/kubernetes_aws_detect_suspicious_kubectl_calls.yml @@ -4,9 +4,9 @@ description: "This search provides information on anonymous Kubectl calls with I how_to_implement: "You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs." id: 042a3d32-8318-4763-9679-09db2644a8f2 known_false_positives: "Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets" -name: "AWS EKS Kubernetes detect suspicious kubectl calls" +name: "Kubernetes AWS detect suspicious kubectl calls" references: [] -search: "sourcetype=aws:cloudwatchlogs:eks userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 src_user=system:anonymous | table src_ip src_user verb userAgent requestURI | stats count by src_ip src_user verb userAgent requestURI |`kubernetes_aws_detect_suspicious_kubectl_calls`" +search: "`aws_cloudwatchlogs_eks` userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 src_user=system:anonymous | table src_ip src_user verb userAgent requestURI | stats count by src_ip src_user verb userAgent requestURI |`kubernetes_aws_detect_suspicious_kubectl_calls_filter`" tags: analytics_story: - "Kubernetes Sensitive Object Access Activity" diff --git a/detections/kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace.yml b/detections/kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace.yml index c750867252..3defb5fe1b 100644 --- a/detections/kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace.yml +++ b/detections/kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace.yml @@ -4,9 +4,9 @@ description: "This search provides information on Kubernetes service accounts,ac how_to_implement: "You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics" id: 55a2264a-b7f0-45e5-addd-1e5ab3415c72 known_false_positives: "Not all service accounts interactions are malicious. Analyst must consider IP and verb context when trying to detect maliciousness." -name: "Azure AKS Kubernetes detect most active service accounts by pod and namespaces" +name: "Kubernetes Azure detect most active service accounts by pod namespace" references: [] -search: "sourcetype:mscs:storage:blob:json category=kube-audit | spath input=properties.log | search user.groups{}=system:serviceaccounts* OR user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow | table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace | top sourceIPs{} user.username verb responseStatus.status properties.pod objectRef.namespace |`kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace`" +search: "`kubernetes_azure` category=kube-audit | spath input=properties.log | search user.groups{}=system:serviceaccounts* OR user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow | table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace | top sourceIPs{} user.username verb responseStatus.status properties.pod objectRef.namespace |`kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace_filter`" tags: analytics_story: - "Kubernetes Sensitive Role Activity" diff --git a/detections/kubernetes_azure_detect_RBAC_authorizations_by_account.yml b/detections/kubernetes_azure_detect_rbac_authorization_by_account.yml similarity index 92% rename from detections/kubernetes_azure_detect_RBAC_authorizations_by_account.yml rename to detections/kubernetes_azure_detect_rbac_authorization_by_account.yml index 159446c803..9186dfae04 100644 --- a/detections/kubernetes_azure_detect_RBAC_authorizations_by_account.yml +++ b/detections/kubernetes_azure_detect_rbac_authorization_by_account.yml @@ -4,9 +4,9 @@ description: "This search provides information on Kubernetes RBAC authorizations how_to_implement: "You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics" id: 47af7d20-0607-4079-97d7-7a29af58b54e known_false_positives: "Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted." -name: "Azure AKS Kubernetes RBAC authorizations by account" +name: "Kubernetes Azure detect RBAC authorization by account" references: [] -search: "sourcetype:mscs:storage:blob:json category=kube-audit | spath input=properties.log | search annotations.authorization.k8s.io/reason=* | table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason |stats count by user.username annotations.authorization.k8s.io/reason | rare user.username annotations.authorization.k8s.io/reason |`kubernetes_azure_detect_RBAC_authorization_by_account`" +search: "sourcetype:mscs:storage:blob:json category=kube-audit | spath input=properties.log | search annotations.authorization.k8s.io/reason=* | table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason |stats count by user.username annotations.authorization.k8s.io/reason | rare user.username annotations.authorization.k8s.io/reason |`kubernetes_azure_detect_rbac_authorization_by_account_filter`" tags: analytics_story: - "Kubernetes Sensitive Role Activity" diff --git a/detections/kubernetes_azure_detect_sensitive_object_access.yml b/detections/kubernetes_azure_detect_sensitive_object_access.yml index 95b86696f1..d506228c52 100644 --- a/detections/kubernetes_azure_detect_sensitive_object_access.yml +++ b/detections/kubernetes_azure_detect_sensitive_object_access.yml @@ -4,9 +4,9 @@ description: "This search provides information on Kubernetes accounts accessing how_to_implement: "You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics" id: 1bba382b-07fd-4ffa-b390-8002739b76e8 known_false_positives: "Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection." -name: "Azure AKS Kubernetes cluster sensitive object access detection" +name: "Kubernetes Azure detect sensitive object access" references: [] -search: "sourcetype=mscs:storage:blob:json category=kube-audit | spath input=properties.log| search objectRef.resource=secrets OR configmaps user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow |table user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason |dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_object_access`" +search: "`kubernetes_azure` category=kube-audit | spath input=properties.log| search objectRef.resource=secrets OR configmaps user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow |table user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason |dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_object_access_filter`" tags: analytics_story: - "Kubernetes Sensitive Object Access Activity" diff --git a/detections/kubernetes_azure_detect_sensitive_role_access.yml b/detections/kubernetes_azure_detect_sensitive_role_access.yml index 7c229c6a61..0201873217 100644 --- a/detections/kubernetes_azure_detect_sensitive_role_access.yml +++ b/detections/kubernetes_azure_detect_sensitive_role_access.yml @@ -4,9 +4,9 @@ description: "This search provides information on Kubernetes accounts accessing how_to_implement: "You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics" id: f27349e5-1641-4f6a-9e68-30402be0ad4c known_false_positives: "Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. " -name: "Azure AKS Kubernetes cluster sensitive role access detection" +name: "Kubernetes Azure detect sensitive role access" references: [] -search: "sourcetype=mscs:storage:blob:json category=kube-audit | spath input=properties.log| search objectRef.resource=clusterroles OR clusterrolebindings | table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason | dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_role_access`" +search: "`kubernetes_azure` category=kube-audit | spath input=properties.log| search objectRef.resource=clusterroles OR clusterrolebindings | table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason | dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_role_access_filter`" tags: analytics_story: - "Kubernetes Sensitive Role Activity" diff --git a/detections/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml b/detections/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml index f10824935e..f13081df0e 100644 --- a/detections/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml +++ b/detections/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml @@ -4,9 +4,9 @@ description: "This search provides information on Kubernetes service accounts wi how_to_implement: "You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics" id: 019690d7-420f-4da0-b320-f27b09961514 known_false_positives: "This search can give false positives as there might be inherent issues with authentications and permissions at cluster." -name: "Azure AKS Kubernetes cluster sensitive object access detection" +name: "Kubernetes Azure detect service accounts forbidden failure access" references: [] -search: "sourcetype=mscs:storage:blob:json category=kube-audit | spath input=properties.log | search user.groups{}=system:serviceaccounts* responseStatus.reason=Forbidden | table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace |`kubernetes_azure_detect_service_accounts_forbidden_failure_access`" +search: "`kubernetes_azure` category=kube-audit | spath input=properties.log | search user.groups{}=system:serviceaccounts* responseStatus.reason=Forbidden | table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace |`kubernetes_azure_detect_service_accounts_forbidden_failure_access_filter`" tags: analytics_story: - "Kubernetes Sensitive Object Access Activity" diff --git a/detections/kubernetes_azure_detect_suspicious_kubectl_calls.yml b/detections/kubernetes_azure_detect_suspicious_kubectl_calls.yml index 313de9a542..480a9c5dce 100644 --- a/detections/kubernetes_azure_detect_suspicious_kubectl_calls.yml +++ b/detections/kubernetes_azure_detect_suspicious_kubectl_calls.yml @@ -4,9 +4,9 @@ description: "This search provides information on rare Kubectl calls with IP, ve how_to_implement: "You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics" id: 4b6d1ba8-0000-4cec-87e6-6cbbd71651b5 known_false_positives: "Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially suspicious IPs and sensitive objects such as configmaps or secrets" -name: "Azure AKS Kubernetes detect suspicious kubectl calls" +name: "Kubernetes Azure detect suspicious kubectl calls" references: [] -search: "sourcetype=mscs:storage:blob:json category=kube-audit | spath input=properties.log | spath input=responseObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration | search userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 | table sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI | rare sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI|`kubernetes_azure_detect_suspicious_kubectl_calls`" +search: "`kubernetes_azure` category=kube-audit | spath input=properties.log | spath input=responseObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration | search userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 | table sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI | rare sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI|`kubernetes_azure_detect_suspicious_kubectl_calls_filter`" tags: analytics_story: - "Kubernetes Sensitive Object Access Activity" diff --git a/detections/kubernetes_azure_pod_scan_fingerprint.yml b/detections/kubernetes_azure_pod_scan_fingerprint.yml index 68d2c2b6c0..4dad0447e8 100644 --- a/detections/kubernetes_azure_pod_scan_fingerprint.yml +++ b/detections/kubernetes_azure_pod_scan_fingerprint.yml @@ -4,9 +4,9 @@ description: "This search provides information of unauthenticated requests via s how_to_implement: "You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics" id: 86aad3e0-732f-4f66-bbbc-70df448e461d known_false_positives: "Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context." -name: "Azure AKS Kubernetes cluster pod scan detection" +name: "Kubernetes Azure pod scan fingerprint" references: [] -search: "sourcetype=mscs:storage:blob:json category=kube-audit | spath input=properties.log | search responseStatus.code=401 | table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod |`kubernetes_azure_pod_scan_fingerprint_detection`" +search: "`kubernetes_azure` category=kube-audit | spath input=properties.log | search responseStatus.code=401 | table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod |`kubernetes_azure_pod_scan_fingerprint_filter`" tags: analytics_story: - "Kubernetes Scanning Activity" diff --git a/detections/kubernetes_azure_scan_fingerprint.yml b/detections/kubernetes_azure_scan_fingerprint.yml index f4914e37b5..0957b1d0ed 100644 --- a/detections/kubernetes_azure_scan_fingerprint.yml +++ b/detections/kubernetes_azure_scan_fingerprint.yml @@ -4,9 +4,9 @@ description: "This search provides information of unauthenticated requests via s how_to_implement: "You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics" id: c5e5bd5c-1013-4841-8b23-e7b3253c840a known_false_positives: "Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context." -name: "Azure AKS Kubernetes cluster scan detection" +name: "Kubernetes Azure scan fingerprint" references: [] -search: "sourcetype=mscs:storage:blob:json category=kube-audit | spath input=properties.log | search responseStatus.code=401 | table sourceIPs{} userAgent verb requestURI responseStatus.reason |`kubernetes_azure_scan_fingerprint_detection`" +search: "`kubernetes_azure` category=kube-audit | spath input=properties.log | search responseStatus.code=401 | table sourceIPs{} userAgent verb requestURI responseStatus.reason |`kubernetes_azure_scan_fingerprint_filter`" tags: analytics_story: - "Kubernetes Scanning Activity" 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 index 2217d8c5f7..c1b4b686bc 100644 --- a/detections/malicious_powershell_process___connect_to_internet_with_hidden_window.yml +++ b/detections/malicious_powershell_process___connect_to_internet_with_hidden_window.yml @@ -26,8 +26,8 @@ known_false_positives: Legitimate process can have this combination of command-l options, but it's not common. tags: analytics_story: - - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns - Malicious PowerShell + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns mitre_attack_id: - T1086 - T1064 diff --git a/detections/monitor_email_for_brand_abuse.yml b/detections/monitor_email_for_brand_abuse.yml index 7889964dfc..aa409d05fa 100644 --- a/detections/monitor_email_for_brand_abuse.yml +++ b/detections/monitor_email_for_brand_abuse.yml @@ -21,8 +21,8 @@ search: '| tstats `security_content_summariesonly` values(All_Email.recipient) a known_false_positives: None at this time tags: analytics_story: - - Suspicious Emails - Brand Monitoring + - Suspicious Emails kill_chain_phases: - Delivery cis20: diff --git a/detections/new_container_uploaded_to_aws_ecr.yml b/detections/new_container_uploaded_to_aws_ecr.yml index 78800dfcdf..8dbb7808cf 100644 --- a/detections/new_container_uploaded_to_aws_ecr.yml +++ b/detections/new_container_uploaded_to_aws_ecr.yml @@ -24,3 +24,5 @@ tags: - Container Implantation Monitoring and Investigation security_domain: threat asset_type: AWS ECR container + mitre_attack_id: + - T1525 diff --git a/detections/okta_account_lockout_events.yml b/detections/okta_account_lockout_events.yml index 1b6c01b38a..162e5d18a4 100644 --- a/detections/okta_account_lockout_events.yml +++ b/detections/okta_account_lockout_events.yml @@ -9,7 +9,7 @@ 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` ' + 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. diff --git a/detections/okta_failed_sso_attempts.yml b/detections/okta_failed_sso_attempts.yml index 2bb6d30ae0..9f1b9c370e 100644 --- a/detections/okta_failed_sso_attempts.yml +++ b/detections/okta_failed_sso_attempts.yml @@ -9,7 +9,7 @@ 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` ' + 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: diff --git a/detections/okta_user_logins_from_multiple_cities.yml b/detections/okta_user_logins_from_multiple_cities.yml index b2257e06f6..416cf29617 100644 --- a/detections/okta_user_logins_from_multiple_cities.yml +++ b/detections/okta_user_logins_from_multiple_cities.yml @@ -12,7 +12,7 @@ search: '`okta` displayMessage="User login to Okta" client.geographicalContext.c | 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' + | `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 diff --git a/detections/processes_launching_netsh.yml b/detections/processes_launching_netsh.yml index 5bd7aa4cc1..d8b134844c 100644 --- a/detections/processes_launching_netsh.yml +++ b/detections/processes_launching_netsh.yml @@ -1,7 +1,7 @@ name: Processes launching netsh id: b89919ed-fe5f-492c-b139-95dbb162040e -version: 2 -date: '2018-11-02' +version: 3 +date: '2020-07-10' 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 @@ -12,12 +12,15 @@ how_to_implement: To successfully implement this search, you must be ingesting d 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) +author: Josef Kuepker, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.process) AS 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`' + where Processes.process_name=netsh.exe by Processes.parent_process_name 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. @@ -27,8 +30,8 @@ tags: - Disabling Security Tools - DHS Report TA18-074A mitre_attack_id: - - T1059 - - T1089 + - T1562.004 + - T1562 kill_chain_phases: - Actions on Objectives cis20: diff --git a/detections/prohibited_network_traffic_allowed.yml b/detections/prohibited_network_traffic_allowed.yml index 4f5c2efa47..abcba4ef5b 100644 --- a/detections/prohibited_network_traffic_allowed.yml +++ b/detections/prohibited_network_traffic_allowed.yml @@ -23,9 +23,9 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime known_false_positives: None identified tags: analytics_story: - - Command and Control - - Ransomware - Prohibited Traffic Allowed or Protocol Mismatch + - Ransomware + - Command and Control mitre_attack_id: - T1043 - T1048 diff --git a/detections/prohibited_software_on_endpoint.yml b/detections/prohibited_software_on_endpoint.yml index 4394c1a7d1..b402bc90bb 100644 --- a/detections/prohibited_software_on_endpoint.yml +++ b/detections/prohibited_software_on_endpoint.yml @@ -24,9 +24,9 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime known_false_positives: None identified tags: analytics_story: - - SamSam Ransomware - - 'Emotet Malware DHS Report TA18-201A ' - Monitor for Unauthorized Software + - 'Emotet Malware DHS Report TA18-201A ' + - SamSam Ransomware kill_chain_phases: - Installation - Command and Control diff --git a/detections/protocol_or_port_mismatch.yml b/detections/protocol_or_port_mismatch.yml index 315424a182..0753a62007 100644 --- a/detections/protocol_or_port_mismatch.yml +++ b/detections/protocol_or_port_mismatch.yml @@ -26,8 +26,8 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime known_false_positives: None identified tags: analytics_story: - - Command and Control - Prohibited Traffic Allowed or Protocol Mismatch + - Command and Control mitre_attack_id: - T1043 kill_chain_phases: diff --git a/detections/reg_exe_manipulating_windows_services_registry_keys.yml b/detections/reg_exe_manipulating_windows_services_registry_keys.yml index 23ce39e78b..1d98e75a92 100644 --- a/detections/reg_exe_manipulating_windows_services_registry_keys.yml +++ b/detections/reg_exe_manipulating_windows_services_registry_keys.yml @@ -1,36 +1,27 @@ name: Reg exe Manipulating Windows Services Registry Keys id: 8470d755-0c13-45b3-bd63-387a373c10cf -version: 2 -date: '2019-03-01' +version: 3 +date: '2020-06-15' 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 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`' +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 Processes.process=*reg* Processes.process=*add* Processes.process=*Services* + by Processes.process_id Processes.dest Processes.process | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + | `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 Persistence Techniques - Windows Service Abuse + - Windows Persistence Techniques mitre_attack_id: - - T1050 - - T1031 - - T1089 + - T1058 kill_chain_phases: - Installation cis20: 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 index fafe061d48..212df8aefb 100644 --- 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 @@ -21,9 +21,9 @@ search: '| tstats `security_content_summariesonly` values(Processes.process) as known_false_positives: None at the moment tags: analytics_story: + - Windows Defense Evasion Tactics - Suspicious Windows Registry Activities - Windows Persistence Techniques - - Windows Defense Evasion Tactics kill_chain_phases: - Actions on Objectives cis20: diff --git a/detections/registry_keys_used_for_persistence.yml b/detections/registry_keys_used_for_persistence.yml index e9119259f3..f99bb6c5c0 100644 --- a/detections/registry_keys_used_for_persistence.yml +++ b/detections/registry_keys_used_for_persistence.yml @@ -28,12 +28,12 @@ known_false_positives: There are many legitimate applications that must execute tags: analytics_story: - Suspicious Windows Registry Activities - - Windows Persistence Techniques - - 'Emotet Malware DHS Report TA18-201A ' - - Ransomware + - Suspicious MSHTA Activity - DHS Report TA18-074A - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns - - Suspicious MSHTA Activity + - Ransomware + - Windows Persistence Techniques + - 'Emotet Malware DHS Report TA18-201A ' mitre_attack_id: - T1103 - T1131 diff --git a/detections/remote_desktop_network_traffic.yml b/detections/remote_desktop_network_traffic.yml index 60065ade2e..e3e4fbe976 100644 --- a/detections/remote_desktop_network_traffic.yml +++ b/detections/remote_desktop_network_traffic.yml @@ -1,7 +1,7 @@ name: Remote Desktop Network Traffic id: 272b8407-842d-4b3d-bead-a704584003d3 -version: 2 -date: '2020-01-10' +version: 3 +date: '2020-07-07' 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 @@ -31,7 +31,7 @@ tags: - Hidden Cobra Malware - Lateral Movement mitre_attack_id: - - T1076 + - T1021.001 kill_chain_phases: - Actions on Objectives cis20: diff --git a/detections/remote_registry_key_modifications.yml b/detections/remote_registry_key_modifications.yml index c900953e4f..3f7f0f342c 100644 --- a/detections/remote_registry_key_modifications.yml +++ b/detections/remote_registry_key_modifications.yml @@ -20,9 +20,9 @@ known_false_positives: This technique may be legitimately used by administrators 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 - - Windows Defense Evasion Tactics kill_chain_phases: - Actions on Objectives cis20: diff --git a/detections/sc_exe_manipulating_windows_services.yml b/detections/sc_exe_manipulating_windows_services.yml index 955142e712..2301370072 100644 --- a/detections/sc_exe_manipulating_windows_services.yml +++ b/detections/sc_exe_manipulating_windows_services.yml @@ -1,14 +1,9 @@ name: Sc exe Manipulating Windows Services id: f0c693d8-2a89-4ce7-80b4-98fea4c3ea6d -version: 2 -date: '2019-02-27' +version: 3 +date: '2020-06-15' 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 @@ -23,15 +18,13 @@ known_false_positives: Using sc.exe to manipulate Windows services is uncommon. and investigate as appropriate. tags: analytics_story: - - Windows Persistence Techniques - Windows Service Abuse - - Disabling Security Tools - DHS Report TA18-074A - Orangeworm Attack Group + - Windows Persistence Techniques + - Disabling Security Tools mitre_attack_id: - T1050 - - T1031 - - T1089 kill_chain_phases: - Installation cis20: diff --git a/detections/short_lived_windows_accounts.yml b/detections/short_lived_windows_accounts.yml index ed209295c9..118bd14327 100644 --- a/detections/short_lived_windows_accounts.yml +++ b/detections/short_lived_windows_accounts.yml @@ -1,7 +1,7 @@ name: Short Lived Windows Accounts id: b25f6f62-0782-43c1-b403-083231ffd97d -version: 1 -date: '2018-01-05' +version: 2 +date: '2020-07-06' 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 @@ -23,7 +23,7 @@ tags: analytics_story: - Account Monitoring and Controls mitre_attack_id: - - T1136 + - T1136.001 cis20: - CIS 16 nist: diff --git a/detections/smb_traffic_spike.yml b/detections/smb_traffic_spike.yml index c43de1b40b..013f2661fc 100644 --- a/detections/smb_traffic_spike.yml +++ b/detections/smb_traffic_spike.yml @@ -21,9 +21,9 @@ known_false_positives: A file server may experience high-demand loads that could this analytic to trigger. tags: analytics_story: - - Ransomware - 'Emotet Malware DHS Report TA18-201A ' - Hidden Cobra Malware + - Ransomware - DHS Report TA18-074A mitre_attack_id: - T1043 diff --git a/detections/smb_traffic_spike___mltk.yml b/detections/smb_traffic_spike___mltk.yml index f848ea5e7e..8cc00847f4 100644 --- a/detections/smb_traffic_spike___mltk.yml +++ b/detections/smb_traffic_spike___mltk.yml @@ -42,9 +42,9 @@ known_false_positives: If you are seeing more results than desired, you may cons the `smb_traffic_spike_mltk_filter` macro to filter out false positive results tags: analytics_story: - - Ransomware - 'Emotet Malware DHS Report TA18-201A ' - Hidden Cobra Malware + - Ransomware - DHS Report TA18-074A mitre_attack_id: - T1043 diff --git a/detections/spectre_and_meltdown_vulnerable_systems.yml b/detections/spectre_and_meltdown_vulnerable_systems.yml index 07973b75f7..199d0e1fca 100644 --- a/detections/spectre_and_meltdown_vulnerable_systems.yml +++ b/detections/spectre_and_meltdown_vulnerable_systems.yml @@ -12,7 +12,8 @@ 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)` + by Vulnerabilities.dest | `drop_dm_object_name(Vulnerabilities)` | `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. diff --git a/detections/suspicious_email_attachment_extensions.yml b/detections/suspicious_email_attachment_extensions.yml index 8614d04a60..0df813263e 100644 --- a/detections/suspicious_email_attachment_extensions.yml +++ b/detections/suspicious_email_attachment_extensions.yml @@ -26,8 +26,8 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime known_false_positives: None identified tags: analytics_story: - - Suspicious Emails - 'Emotet Malware DHS Report TA18-201A ' + - Suspicious Emails kill_chain_phases: - Delivery cis20: diff --git a/detections/suspicious_reg_exe_process.yml b/detections/suspicious_reg_exe_process.yml index 39ce10bfee..c60be0872b 100644 --- a/detections/suspicious_reg_exe_process.yml +++ b/detections/suspicious_reg_exe_process.yml @@ -29,8 +29,8 @@ known_false_positives: It's possible for system administrators to write scripts to filter them out. tags: analytics_story: - - Disabling Security Tools - Windows Defense Evasion Tactics + - Disabling Security Tools - DHS Report TA18-074A mitre_attack_id: - T1112 diff --git a/detections/system_processes_run_from_unexpected_locations.yml b/detections/system_processes_run_from_unexpected_locations.yml index 4e7d43fa11..7e77f9ed36 100644 --- a/detections/system_processes_run_from_unexpected_locations.yml +++ b/detections/system_processes_run_from_unexpected_locations.yml @@ -21,8 +21,8 @@ known_false_positives: None identified tags: analytics_story: - Suspicious Command-Line Executions - - Ransomware - Unusual Processes + - Ransomware mitre_attack_id: - T1036 kill_chain_phases: diff --git a/detections/tor_traffic.yml b/detections/tor_traffic.yml index 83feca13ac..ae0ccd1df0 100644 --- a/detections/tor_traffic.yml +++ b/detections/tor_traffic.yml @@ -21,9 +21,9 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime known_false_positives: None at this time tags: analytics_story: - - Command and Control - - Ransomware - Prohibited Traffic Allowed or Protocol Mismatch + - Ransomware + - Command and Control mitre_attack_id: - T1043 kill_chain_phases: diff --git a/detections/unsigned_image_loaded_by_LSASS.yml b/detections/unsigned_image_loaded_by_lsass.yml similarity index 99% rename from detections/unsigned_image_loaded_by_LSASS.yml rename to detections/unsigned_image_loaded_by_lsass.yml index e9646fe171..eb06175fb6 100644 --- a/detections/unsigned_image_loaded_by_LSASS.yml +++ b/detections/unsigned_image_loaded_by_lsass.yml @@ -23,7 +23,7 @@ tags: analytics_story: - Credential Dumping mitre_attack_id: - - T1003 + - T1003.001 kill_chain_phases: - Actions on Objectives cis20: diff --git a/detections/unusually_long_command_line.yml b/detections/unusually_long_command_line.yml index f3f2c703fa..434cd4e457 100644 --- a/detections/unusually_long_command_line.yml +++ b/detections/unusually_long_command_line.yml @@ -16,16 +16,16 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime 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) + 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: - - Ransomware - Suspicious Command-Line Executions - - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns - Unusual Processes + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Ransomware kill_chain_phases: - Actions on Objectives cis20: diff --git a/detections/unusually_long_command_line___mltk.yml b/detections/unusually_long_command_line___mltk.yml index 9f50cca4cf..dea463a213 100644 --- a/detections/unusually_long_command_line___mltk.yml +++ b/detections/unusually_long_command_line___mltk.yml @@ -35,10 +35,10 @@ known_false_positives: Some legitimate applications use long command lines for i build the associated model. tags: analytics_story: - - Ransomware - Suspicious Command-Line Executions - - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns - Unusual Processes + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Ransomware kill_chain_phases: - Actions on Objectives cis20: diff --git a/detections/web_fraud___account_harvesting.yml b/detections/web_fraud___account_harvesting.yml index d02cf467a3..e3a7425763 100644 --- a/detections/web_fraud___account_harvesting.yml +++ b/detections/web_fraud___account_harvesting.yml @@ -19,8 +19,8 @@ references: 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> + | 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 diff --git a/detections/windows_event_log_cleared.yml b/detections/windows_event_log_cleared.yml index ee1b44198c..190f70bd53 100644 --- a/detections/windows_event_log_cleared.yml +++ b/detections/windows_event_log_cleared.yml @@ -1,7 +1,7 @@ name: Windows Event Log Cleared id: ad517544-aff9-4c96-bd99-d6eb43bfbb6a -version: 3 -date: '2019-02-27' +version: 4 +date: '2020-07-06' description: 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 @@ -9,9 +9,8 @@ how_to_implement: To successfully implement this search, you need to be ingestin 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)` +search: (`wineventlog_security` (EventID=1102 OR EventID=1100)) OR (`wineventlog_system` EventID=104) + | stats count min(_time) as firstTime max(_time) as lastTime by EventID dest | `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. @@ -20,7 +19,7 @@ tags: - Windows Log Manipulation - Ransomware mitre_attack_id: - - T1070 + - T1551.001 kill_chain_phases: - Actions on Objectives cis20: diff --git a/docs/CONTRIBUTING.md b/docs/CONTRIBUTING.md deleted file mode 100644 index 0829e2aa16..0000000000 --- a/docs/CONTRIBUTING.md +++ /dev/null @@ -1,130 +0,0 @@ -# Contributing to the Project - -This document is the single source of truth on how to contribute to this codebase. Please feel free to browse the open issues and file new ones. All feedback is welcome! - ----- - -## Topics - -* [Prerequisites](#prerequisites) - * [Contributor License Agreement](#contributor-license-agreement) - * [Code of Conduct](#code-of-conduct) - * [Setup Development Environment](#setup-development-environment) -* [Contribution Workflow](#contribution-workflow) - * [Feature Requests and Bug Reports](#feature-requests-and-bug-reports) - * [Fixing Issues](#fixing-issues) - * [Pull Requests](#pull-requests) - * [Code Review](#code-review) - * [Documentation](#documentation) -* [Maintainers](#maintainers) - ----- - -## Prerequisites -When contributing to this repository, please first discuss the change you wish to make via a GitHub issue, Slack message, email, or via other channels with the owners of this repository. - -##### Contributor License Agreement -At the moment, we can only accept pull requests submitted from either: -* Splunk employees or -* Individuals that have signed our contributors' agreement - -If you wish to be a contributing member of our community, please see the agreement [for individuals](https://www.splunk.com/goto/individualcontributions) or [for organizations](https://www.splunk.com/goto/contributions). - -##### Code of Conduct -Please make sure to read and observe our [Code of Conduct](contributing/code-of-conduct.md). Please follow it in all of your interactions involving the project. - -##### Setup Development Environment -see [Developing section](https://github.com/splunk/security-content#developing) - -## Contribution Workflow -Help is always welcome! For example, documentation can always use improvement. There's always code that can be clarified, functionality that can be extended, and tests to be added to guarantee behavior. If you see something you think should be fixed, don't be afraid to own it. - -##### Feature Requests and Bug Reports -Have ideas on improvements? See something that needs work? While the community encourages everyone to contribute code, it is also appreciated when someone reports an issue. Please report any issues or bugs you find through [GitHub's issue tracker](https://github.com/splunk/security-content/issues). - -If you are reporting a bug, please include: - -* Your operating system name and version -* Any details about your local setup that might be helpful in troubleshooting (ex. Python interpreter version, Splunk version, etc.) -* Detailed steps to reproduce the bug - -We'd also like to hear about your propositions and suggestions. Feel free to submit them as issues and: - -* Explain in detail how they should work -* Note that keeping the scope as narrow as possible will make the suggestion easier to implement - -##### Fixing Issues -Look through our [issue tracker](https://github.com/splunk/security-content/issues) to find problems to fix! Feel free to comment and tag corresponding stakeholders or full-time maintainers of this project with any questions or concerns. - -##### Pull Requests -What is a "pull request"? It informs the project's core developers about the changes you want to review and merge. Once you submit a pull request, it enters a stage of code review where you and others can discuss its potential modifications and maybe even add more commits to it later on. - -If you want to learn more, please consult this [tutorial on how pull requests work](https://help.github.com/articles/using-pull-requests/) in the [GitHub Help Center](https://help.github.com/). - -Here's an overview of how you can make a pull request against this project: - -1. Fork the [analytic\_story\_execution GitHub repository](https://github.com/splunk/security-content/issues) -2. Clone your fork using git and create a branch off of develop - - ``` - $ git clone git@github.com:YOUR_GITHUB_USERNAME/security-content.git - $ cd security-content - - # This project uses 'develop' for all development activity, so create your branch off that - $ git checkout -b your-bugfix-branch-name develop - ``` - -3. Make your changes, commit, and push (once your tests have passed) - - ``` - $ cd security-content - $ git commit -m "" - $ git push - ``` - -4. Submit a pull request through the GitHub website, using the changes from your forked codebase - -##### Code Review -There are two aspects of code review: giving and receiving. - -To make it easier for your PR to receive reviews, keep in mind that the reviewers will need you to: -* Follow the project coding conventions -* Write good commit messages -* Break large changes into a logical series of smaller patches which individually make easily understandable changes, and in aggregate solve a broader issue - -Reviewers, the people providing the review, are highly encouraged to revisit the [Code of Conduct](contributing/code-of-conduct.md) and must go above and beyond to promote a collaborative, respectful community. - -When reviewing PRs from others, [The Gentle Art of Patch Review](http://sage.thesharps.us/2014/09/01/the-gentle-art-of-patch-review/) suggests an iterative series of focuses designed to lead new contributors to positive collaboration, such as: - -* Is the idea behind the contribution sound? -* Is the contribution architected correctly? -* Is the contribution polished? - -For this project, we require at least one approval. A build from our continuous integration system must also be successful off of your branch. Please note that any new changes made with your existing pull request during review will automatically unapprove and retrigger another build/round of tests. - -##### Documentation -We can always use improvements to our documentation! Anyone can contribute to these docs--whether you’re new to the project, you’ve been around a long time, or if you just can’t stand seeing typos. - -Here's what's needed? - -1. More complementary documentation. Have you something unclear? -2. More examples or generic templates that others can use. -3. Blog posts, articles, and such are all very appreciated. - -You can also edit documentation files directly in the GitHub web interface, without creating a local copy. This can be convenient for small typos or grammar fixes. - -## Maintainers - -If you need help, feel free to tag one of the active maintainers of this project in a post or comment. We'll do our best to reach out to you as quickly as we can. - -``` -# Active maintainers marked with (*) - -(*) Bhavin Patel -(*) David Dorsey -(*) Jose Hernandez -(*) Rico Valdez -(*) Patrick Bareib -(*) Brianna Blacet -``` - diff --git a/docs/README.md b/docs/README.md index dda91ce796..65fc135a1b 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,9 +1,9 @@ -# Welcome to Splunk Security Research! +# Splunk Security Content ![](static/logo.png) -Thanks for stopping by the Splunk Security Research Team's resource portal! Here you'll find background and links to our security content and other related tools. +Welcome to the Splunk Security Content -Splunk security content is organized into "Analytic Stories," themed security guides 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 built to work together to detect, investigate, and respond to threats. The other apps were designed to help you derive more value from this 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. ## View Our Content You can review our Analytic Stories by category [here](stories_categories.md), or in our [Splunk App](https://github.com/splunk/security-content/releases). @@ -13,38 +13,40 @@ If you prefer working with the command line, check out our [API](https://docs.sp ``` 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" } ``` ## Getting Started -Once you've cloned the security-content repo, we recommend using our Analytic Story Execution App [(ASX)](https://github.com/splunk/analytics_story_execution) to execute all of the searches, machine-learning models, and Splunk Phantom playbooks in the story automatically. +Once you've installed our [app](https://github.com/splunk/security-content/releases), we recommend using our Analytic Story Execution App [(ASX)](https://github.com/splunk/analytics_story_execution) to execute and schedule all of the detections a story automatically. ## Test Out The Detections -The [attack_range](https://http://github.com/splunk/attack_range) project allows you to spin up an enviroment and launch attacks against it to test the detections. +The [attack_range](https://github.com/splunk/attack_range) project allows you to spin up an enviroment and launch attacks against it to test the detections. ## Questions? If you get stuck or need help with any of our tools, see our [support options](https://github.com/splunk/security-content#support). ## Contribute Content -If you want to help the rest of the security community by sharing your own detections, see our [contributor guide](https://github.com/splunk/security-content#Contributing). Digital defenders unite! +If you want to help the rest of the security community by sharing your own detections, see our [contributor guide](https://github.com/splunk/security-content/blob/develop/docs/CONTRIBUTING.md). Digital defenders unite! -## Content Spec Documentation -* [Story](spec/story.spec.md) -* [Detections](spec/detections.spec.md) -* [Investigations](spec/investigations.spec.md) -* [Responses](spec/responses.spec.md) -* [Baselines](spec/baselines.spec.md) +## Content Parts +* [stories/](https://github.com/splunk/security-content/tree/develop/stories): All Analytic Stories +* [detections/](https://github.com/splunk/security-content/tree/develop/detections): Splunk Enterprise, Splunk UBA, and Splunk Phantom detections that power Analytic Stories +* [response_tasks/](https://github.com/splunk/security-content/tree/develop/response_tasks): Splunk Enterprise and Splunk Phantom investigative searches and playbooks employed by Analytic Stories +* [responses/](https://github.com/splunk/security-content/tree/develop/responses): Automated Splunk Enterprise and Splunk Phantom responses triggered by Analytic Stories +* [baselines/](https://github.com/splunk/security-content/tree/develop/baselines): Splunk Phantom and Splunk Enterprise baseline searches needed to support detection searches in Analytic Stories + +#### Content Spec Files +* [stories](https://github.com/splunk/security-content/blob/develop/docs/spec/stories.spec.md) +* [detections](https://github.com/splunk/security-content/blob/develop/docs/spec/detections.spec.md) +* [deployments](https://github.com/splunk/security-content/blob/develop/docs/spec/deployments.spec.md) +* [responses](https://github.com/splunk/security-content/blob/develop/docs/spec/responses.spec.md) +* [response_tasks](https://github.com/splunk/security-content/blob/develop/docs/spec/response_tasks.spec.md) +* [baselines](https://github.com/splunk/security-content/blob/develop/docs/spec/baselines.spec.md) +* [lookups](https://github.com/splunk/security-content/blob/develop/docs/spec/lookups.spec.md) +* [macros](https://github.com/splunk/security-content/blob/develop/docs/spec/macros.spec.md) diff --git a/docs/contributing/code-of-conduct.md b/docs/contributing/code-of-conduct.md deleted file mode 100644 index 09408f440e..0000000000 --- a/docs/contributing/code-of-conduct.md +++ /dev/null @@ -1,73 +0,0 @@ -## Code of Conduct - -### Our Pledge - -In the interest of fostering an open and welcoming environment, we as -contributors and maintainers pledge to making participation in our project and -our community a harassment-free experience for everyone, regardless of age, body -size, disability, ethnicity, gender identity and expression, level of experience, -nationality, personal appearance, race, religion, or sexual identity and -orientation. - -### Our Standards - -Examples of behavior that contributes to creating a positive environment -include: - -* Using welcoming and inclusive language -* Being respectful of differing viewpoints and experiences -* Gracefully accepting constructive criticism -* Focusing on what is best for the community -* Showing empathy towards other community members - -Examples of unacceptable behavior by participants include: - -* The use of sexualized language or imagery and unwelcome sexual attention or -advances -* Trolling, insulting/derogatory comments, and personal or political attacks -* Public or private harassment -* Publishing others' private information, such as a physical or electronic address, without explicit permission -* Other conduct which could reasonably be considered inappropriate in a professional setting - -### Our Responsibilities - -Project maintainers are responsible for clarifying the standards of acceptable -behavior and are expected to take appropriate and fair corrective action in -response to any instances of unacceptable behavior. - -Project maintainers have the right and responsibility to remove, edit, or -reject comments, commits, code, wiki edits, issues, and other contributions -that are not aligned to this Code of Conduct, or to ban temporarily or -permanently any contributor for other behaviors that they deem inappropriate, -threatening, offensive, or harmful. - -### Scope - -This Code of Conduct applies both within project spaces and in public spaces -when an individual is representing the project or its community. Examples of -representing a project or community include using an official project e-mail -address, posting via an official social media account, or acting as an appointed -representative at an online or offline event. Representation of a project may be -further defined and clarified by project maintainers. - -### Enforcement - -Instances of abusive, harassing, or otherwise unacceptable behavior may be -reported by contacting the project team at support@splunk.com. All -complaints will be reviewed and investigated and will result in a response that -is deemed necessary and appropriate to the circumstances. The project team is -obligated to maintain confidentiality with regard to the reporter of an incident. -Further details of specific enforcement policies may be posted separately. - -Project maintainers who do not follow or enforce the Code of Conduct in good -faith may face temporary or permanent repercussions as determined by other -members of the project's leadership. - -### Attribution - -This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, -available at [http://contributor-covenant.org/version/1/4][version] - -[homepage]: http://contributor-covenant.org -[version]: http://contributor-covenant.org/version/1/4/ - diff --git a/docs/spec/baselines.spec.json b/docs/spec/baselines.spec.json new file mode 100644 index 0000000000..c13fc47497 --- /dev/null +++ b/docs/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": { + "$id": "#/properties/version", + "default": 0, + "description": "version of baseline, e.g. 1 or 2 ...", + "examples": [ + 1 + ], + "type": "integer" + } + }, + "required": [ + "name", + "id", + "version", + "date", + "description", + "author", + "search", + "tags" + ], + "title": "Baseline Schema", + "type": "object" +} diff --git a/docs/spec/deployments.spec.json b/docs/spec/deployments.spec.json new file mode 100644 index 0000000000..c417cc0bf3 --- /dev/null +++ b/docs/spec/deployments.spec.json @@ -0,0 +1,264 @@ +{ + "$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/docs/spec/detections.spec.json b/docs/spec/detections.spec.json new file mode 100644 index 0000000000..9f9fc25482 --- /dev/null +++ b/docs/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": { + "$id": "#/properties/version", + "default": 0, + "description": "version of detection, e.g. 1 or 2 ...", + "examples": [ + 2 + ], + "type": "integer" + } + }, + "required": [ + "name", + "id", + "version", + "date", + "description", + "type", + "author", + "search", + "known_false_positives", + "tags" + ], + "title": "Detection Schema", + "type": "object" +} diff --git a/docs/spec/lookups.spec.json b/docs/spec/lookups.spec.json new file mode 100644 index 0000000000..0c85720259 --- /dev/null +++ b/docs/spec/lookups.spec.json @@ -0,0 +1,88 @@ +{ + "$id": "https://api.splunkresearch.com/schemas/lookups.json", + "$schema": "http://json-schema.org/draft-07/schema#", + "description": "A object that defines a lookup file and its properties.", + "oneOf": [ + { + "required": [ + "collection" + ] + }, + { + "required": [ + "filename" + ] + } + ], + "properties": { + "case_sensitive_match": { + "description": "What the macro is intended to filter", + "enum": [ + "true", + "false" + ], + "examples": [ + "true" + ], + "type": "string" + }, + "collection": { + "description": "Name of the collection to use for this lookup", + "examples": [ + "prohibited_apps_launching_cmd" + ], + "type": "string" + }, + "default_match": { + "description": "The default value if no match is found", + "examples": [ + "true" + ], + "type": "string" + }, + "description": { + "description": "The description of this lookup", + "examples": [ + "This lookup contains file names that exist in the Windows\\System32 directory" + ], + "type": "string" + }, + "filename": { + "description": "The name of the file to use for this lookup", + "examples": [ + "prohibited_apps_launching_cmd.csv" + ], + "type": "string" + }, + "match_type": { + "description": "A comma and space-delimited list of () specification to allow for non-exact matching", + "examples": [ + "WILDCARD(process)" + ], + "type": "string" + }, + "max_matches": { + "description": "The maximum number of possible matches for each input lookup value", + "examples": [ + "100" + ], + "type": "integer" + }, + "min_matches": { + "description": "Minimum number of possible matches for each input lookup value", + "examples": [ + "1" + ], + "type": "integer" + }, + "name": { + "description": "The name of the lookup to be used in searches", + "examples": [ + "isWindowsSystemFile_lookup" + ], + "type": "string" + } + }, + "title": "Lookup Manifest", + "type": "object" +} diff --git a/docs/spec/macros.spec.json b/docs/spec/macros.spec.json new file mode 100644 index 0000000000..cd0f5cafe1 --- /dev/null +++ b/docs/spec/macros.spec.json @@ -0,0 +1,43 @@ +{ + "$id": "https://api.splunkresearch.com/schemas/macros.json", + "$schema": "http://json-schema.org/draft-07/schema#", + "description": "An object that defines the parameters for a Splunk Macro", + "properties": { + "arguments": { + "description": "A list of the arguments being passed to this macro", + "items": { + "type": "string" + }, + "minItems": 0, + "type": "array", + "uniqueItems": true + }, + "definition": { + "description": "The macro definition", + "examples": [ + "(query=fls-na* AND query = www* AND query=images*)" + ], + "type": "string" + }, + "description": { + "description": "What the macro is intended to filter", + "examples": [ + "Use this macro to filter out known good objects" + ], + "type": "string" + }, + "name": { + "description": "The name of the macro", + "examples": [ + "detection_search_output_filter" + ], + "type": "string" + } + }, + "required": [ + "name", + "description" + ], + "title": "Macro Manifest", + "type": "object" +} diff --git a/docs/spec/response_tasks.spec.json b/docs/spec/response_tasks.spec.json new file mode 100644 index 0000000000..257e12f5fb --- /dev/null +++ b/docs/spec/response_tasks.spec.json @@ -0,0 +1,159 @@ +{ + "$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": { + "$id": "#/properties/version", + "default": 0, + "description": "version of detection, e.g. 1 or 2 ...", + "examples": [ + 3 + ], + "type": "integer" + } + }, + "required": [ + "name", + "id", + "version", + "date", + "description", + "author" + ], + "title": "Response Task Schema", + "type": "object" +} diff --git a/docs/spec/responses.spec.json b/docs/spec/responses.spec.json new file mode 100644 index 0000000000..f14d63f109 --- /dev/null +++ b/docs/spec/responses.spec.json @@ -0,0 +1,117 @@ +{ + "$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": { + "$id": "#/properties/version", + "default": 0, + "description": "version of detection, e.g. 1 or 2 ...", + "examples": [ + 1 + ], + "type": "integer" + } + }, + "required": [ + "name", + "id", + "version", + "date", + "description", + "author", + "response_tasks", + "tags" + ], + "title": "Response Schema", + "type": "object" +} diff --git a/docs/spec/stories.spec.json b/docs/spec/stories.spec.json new file mode 100644 index 0000000000..3eda58b394 --- /dev/null +++ b/docs/spec/stories.spec.json @@ -0,0 +1,106 @@ +{ + "$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/docs/splunk_docs_categories.wiki b/docs/splunk_docs_categories.wiki index ca0c275cb5..52e0034ea4 100644 --- a/docs/splunk_docs_categories.wiki +++ b/docs/splunk_docs_categories.wiki @@ -229,7 +229,7 @@ To get started, run the detection search to identify parent processes of `netsh. * PR.PT ====References==== -* https://technet.microsoft.com/library/bb490939.aspx +* https://docs.microsoft.com/en-us/previous-versions/tn-archive/bb490939(v=technet.10) * 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 @@ -549,7 +549,6 @@ Attackers employ a variety of tactics in order to avoid detection and operate wi ====Tags==== =====ATT&CK===== -* T1031 * T1050 * T1059 * T1089 @@ -663,6 +662,7 @@ If there is evidence of lateral movement, it is imperative for analysts to colle ====Detections==== * Detect Activity Related to Pass the Hash Attacks +* Kerberoasting spn request with RC4 encryption * Remote Desktop Network Traffic * Remote Desktop Process Running On System * Schtasks scheduling job on remote system @@ -677,6 +677,7 @@ If there is evidence of lateral movement, it is imperative for analysts to colle * T1053 * T1075 * T1076 +* T1208 =====Kill Chain Phases===== * Actions on Objectives @@ -684,10 +685,12 @@ If there is evidence of lateral movement, it is imperative for analysts to colle =====CIS===== * CIS 16 * CIS 3 +* CIS 8 * CIS 9 =====NIST===== * DE.AE +* DE.CM * PR.AC * PR.IP @@ -1311,6 +1314,54 @@ version = 2 +===Suspicious Zoom Child Processes=== + +Attackers are using Zoom as an vector to increase privileges on a sytems. This story detects new child processes of zoom and provides investigative actions for this detection. + +
+
+ +====Narrative==== +Zoom is a leader in modern enterprise video communications and its usage has increased dramatically with a large amount of the population under stay-at-home orders due to the COVID-19 pandemic. With increased usage has come increased scrutiny and several security flaws have been found with this application on both Windows and macOS systems.\ +Current detections focus on finding new child processes of this application on a per host basis. Investigative searches are included to gather information needed during an investigation. + +====Detections==== +* Detect Prohibited Applications Spawning cmd exe +* First Time Seen Child Process of Zoom + +====Data Models==== +* Endpoint + +====Tags==== + +=====ATT&CK===== +* T1059 +* T1068 + +=====Kill Chain Phases===== +* Actions on Objectives +* Exploitation + +=====CIS===== +* CIS 3 +* CIS 8 + +=====NIST===== +* DE.CM +* PR.IP +* PR.PT + +====References==== +* https://blog.rapid7.com/2020/04/02/dispelling-zoom-bugbears-what-you-need-to-know-about-the-latest-zoom-vulnerabilities/ +* https://threatpost.com/two-zoom-zero-day-flaws-uncovered/154337/ + +date = 2020-04-13 + +version = 1 + +
+
+ ===Windows Defense Evasion Tactics=== 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 @@ -1443,10 +1494,9 @@ Maintaining persistence is one of the first steps taken by attackers after the i ====Tags==== =====ATT&CK===== -* T1031 * T1050 * T1053 -* T1089 +* T1058 * T1103 * T1131 * T1138 @@ -1568,7 +1618,6 @@ Monitoring user accounts within your enterprise is a critical analytic function * PR.IP ====References==== -* https://www.sans.org/media/critical-security-controls/critical-controls-poster-2016.pdf date = 2017-09-06 @@ -1888,6 +1937,7 @@ This Analytic Story includes searches that will help you monitor your AWS CloudT ====Tags==== =====ATT&CK===== +* T1078 =====Kill Chain Phases===== * Actions on Objectives @@ -1933,6 +1983,8 @@ This Analytic Story is focused on detecting suspicious new instances in your EC2 ====Tags==== =====ATT&CK===== +* T1078 +* T1535 =====Kill Chain Phases===== * Actions on Objectives @@ -2026,6 +2078,7 @@ This Analytic Story was designed to provide you with flexibility in the precisio ====Tags==== =====ATT&CK===== +* T1535 =====Kill Chain Phases===== @@ -2070,6 +2123,8 @@ The detection searches in this Analytic Story are designed to help you uncover A ====Tags==== =====ATT&CK===== +* T1078 +* T1098 =====Kill Chain Phases===== * Actions on Objectives @@ -2121,6 +2176,8 @@ This Analytic Story is focused on detecting suspicious new instances in your clo ====Tags==== =====ATT&CK===== +* T1078 +* T1535 =====Kill Chain Phases===== * Actions on Objectives @@ -2164,6 +2221,7 @@ Container Registrys provide a way for organizations to keep customized images of ====Tags==== =====ATT&CK===== +* T1525 =====Kill Chain Phases===== @@ -2195,6 +2253,8 @@ Kubernetes is the most used container orchestration platform, this orchestration * Amazon EKS Kubernetes Pod scan detection * Amazon EKS Kubernetes cluster scan detection * GCP Kubernetes cluster scan detection +* Kubernetes Azure pod scan fingerprint +* Kubernetes Azure scan fingerprint ====Data Models==== @@ -2219,6 +2279,88 @@ version = 1 +===Kubernetes Sensitive Object Access Activity=== + +This story addresses detection and response of accounts acccesing Kubernetes cluster sensitive objects such as configmaps or secrets providing information on items such as user user, group. object, namespace and authorization reason. + +
+
+ +====Narrative==== +Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive objects within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes sensitive objects. + +====Detections==== +* AWS EKS Kubernetes cluster sensitive object access +* Kubernetes AWS detect service accounts forbidden failure access +* Kubernetes AWS detect suspicious kubectl calls +* Kubernetes Azure detect sensitive object access +* Kubernetes Azure detect service accounts forbidden failure access +* Kubernetes Azure detect suspicious kubectl calls + +====Data Models==== + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== +* Lateral Movement + +=====CIS===== + +=====NIST===== + +====References==== +* https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html + +date = 2020-05-20 + +version = 1 + +
+
+ +===Kubernetes Sensitive Role Activity=== + +This story addresses detection and response around Sensitive Role usage within a Kubernetes clusters against cluster resources and namespaces. + +
+
+ +====Narrative==== +Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive roles within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes role activities + +====Detections==== +* Kubernetes AWS detect RBAC authorization by account +* Kubernetes AWS detect most active service accounts by pod +* Kubernetes AWS detect sensitive role access +* Kubernetes Azure detect RBAC authorization by account +* Kubernetes Azure detect most active service accounts by pod namespace +* Kubernetes Azure detect sensitive role access + +====Data Models==== + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== +* Lateral Movement + +=====CIS===== + +=====NIST===== + +====References==== +* https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html + +date = 2020-05-20 + +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. @@ -2242,6 +2384,8 @@ AWS CloudTrail is an AWS service that helps you enable governance, compliance, a ====Tags==== =====ATT&CK===== +* T1078 +* T1535 =====Kill Chain Phases===== * Actions on Objectives @@ -2287,6 +2431,8 @@ It is important to monitor and control who has access to your AWS infrastructure ====Tags==== =====ATT&CK===== +* T1078 +* T1535 =====Kill Chain Phases===== * Actions on Objectives @@ -2330,6 +2476,7 @@ Among things to look out for are S3 access from unfamiliar locations and by unfa ====Tags==== =====ATT&CK===== +* T1530 =====Kill Chain Phases===== * Actions on Objectives @@ -2396,6 +2543,52 @@ version = 1 +===Suspicious Cloud Authentication Activities=== + +Monitor your cloud authentication events. Searches within this Analytic Story leverage the recent cloud updates to the Authentication data model to help you stay aware of and investigate suspicious login activity. + +
+
+ +====Narrative==== +It is important to monitor and control who has access to your cloud infrastructure. Detecting suspicious logins 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 compute activity whether legitimate or otherwise.\ +This Analytic Story has data model versions of cloud searches leveraging Authentication data, including those looking for suspicious login activity, and cross-account activity for AWS. + +====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 - DM + +====Data Models==== +* Authentication + +====Tags==== + +=====ATT&CK===== +* T1535 + +=====Kill Chain Phases===== +* Actions on Objectives + +=====CIS===== +* CIS 16 + +=====NIST===== +* DE.AE +* DE.DP + +====References==== +* https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/ +* https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html + +date = 2020-06-04 + +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. @@ -2415,6 +2608,7 @@ A common attack technique is to infiltrate a cloud instance and make modificatio ====Tags==== =====ATT&CK===== +* T1078 =====Kill Chain Phases===== @@ -2518,7 +2712,6 @@ Suspicious activities--spikes in SMB traffic, processes that launch netsh (to mo ====Tags==== =====ATT&CK===== -* T1031 * T1043 * T1050 * T1053 @@ -2779,10 +2972,8 @@ This Analytic Story is designed to help you detect and investigate suspicious ac ====Tags==== =====ATT&CK===== -* T1031 * T1050 * T1059 -* T1089 =====Kill Chain Phases===== * Actions on Objectives @@ -3108,9 +3299,8 @@ The Windows operating system uses a services architecture to allow for running c ====Tags==== =====ATT&CK===== -* T1031 * T1050 -* T1089 +* T1058 =====Kill Chain Phases===== * Actions on Objectives diff --git a/docs/stories_categories.md b/docs/stories_categories.md index 0cf7630e17..fd64b747e6 100644 --- a/docs/stories_categories.md +++ b/docs/stories_categories.md @@ -231,7 +231,7 @@ To get started, run the detection search to identify parent processes of `netsh. * PR.PT ##### References -* https://technet.microsoft.com/library/bb490939.aspx +* https://docs.microsoft.com/en-us/previous-versions/tn-archive/bb490939(v=technet.10) * 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 @@ -319,6 +319,8 @@ Another search detects incidents wherein a single password is used across multip * [Suspicious WMI Use](#Suspicious-WMI-Use) +* [Suspicious Zoom Child Processes](#Suspicious-Zoom-Child-Processes) + * [Windows Defense Evasion Tactics](#Windows-Defense-Evasion-Tactics) * [Windows Log Manipulation](#Windows-Log-Manipulation) @@ -559,7 +561,6 @@ Attackers employ a variety of tactics in order to avoid detection and operate wi #### Mappings ##### ATT&CK -* T1031 * T1050 * T1059 * T1089 @@ -661,6 +662,7 @@ If there is evidence of lateral movement, it is imperative for analysts to colle #### Detections * Detect Activity Related to Pass the Hash Attacks +* Kerberoasting spn request with RC4 encryption * Remote Desktop Network Traffic * Remote Desktop Process Running On System * Schtasks scheduling job on remote system @@ -675,6 +677,7 @@ If there is evidence of lateral movement, it is imperative for analysts to colle * T1053 * T1075 * T1076 +* T1208 ##### Kill Chain Phases * Actions on Objectives @@ -682,10 +685,12 @@ If there is evidence of lateral movement, it is imperative for analysts to colle ###### CIS * CIS 16 * CIS 3 +* CIS 8 * CIS 9 ##### NIST * DE.AE +* DE.CM * PR.AC * PR.IP @@ -1236,6 +1241,48 @@ In the event that unauthorized WMI execution occurs, it will be important for an * 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 Zoom Child Processes +* id = aa3749a6-49c7-491e-a03f-4eaee5fe0258 +* date = 2020-04-13 +* version = 1 + +#### Description +Attackers are using Zoom as an vector to increase privileges on a sytems. This story detects new child processes of zoom and provides investigative actions for this detection. + +#### Narrative +Zoom is a leader in modern enterprise video communications and its usage has increased dramatically with a large amount of the population under stay-at-home orders due to the COVID-19 pandemic. With increased usage has come increased scrutiny and several security flaws have been found with this application on both Windows and macOS systems.\ +Current detections focus on finding new child processes of this application on a per host basis. Investigative searches are included to gather information needed during an investigation. + +#### Detections +* Detect Prohibited Applications Spawning cmd exe +* First Time Seen Child Process of Zoom + +#### Data Models +* Endpoint + +#### Mappings + +##### ATT&CK +* T1059 +* T1068 + +##### Kill Chain Phases +* Actions on Objectives +* Exploitation + +###### CIS +* CIS 3 +* CIS 8 + +##### NIST +* DE.CM +* PR.IP +* PR.PT + +##### References +* https://blog.rapid7.com/2020/04/02/dispelling-zoom-bugbears-what-you-need-to-know-about-the-latest-zoom-vulnerabilities/ +* https://threatpost.com/two-zoom-zero-day-flaws-uncovered/154337/ + ### Windows Defense Evasion Tactics * id = 56e24a28-5003-4047-b2db-e8f3c4618064 * date = 2018-05-31 @@ -1357,10 +1404,9 @@ Maintaining persistence is one of the first steps taken by attackers after the i #### Mappings ##### ATT&CK -* T1031 * T1050 * T1053 -* T1089 +* T1058 * T1103 * T1131 * T1138 @@ -1486,7 +1532,6 @@ Monitoring user accounts within your enterprise is a critical analytic function * PR.IP ##### References -* https://www.sans.org/media/critical-security-controls/critical-controls-poster-2016.pdf ### Asset Tracking * id = 91c676cf-0b23-438d-abee-f6335e1fce77 @@ -1753,6 +1798,10 @@ Various legacy protocols operate by default in the clear, without the protection * [Kubernetes Scanning Activity](#Kubernetes-Scanning-Activity) +* [Kubernetes Sensitive Object Access Activity](#Kubernetes-Sensitive-Object-Access-Activity) + +* [Kubernetes Sensitive Role Activity](#Kubernetes-Sensitive-Role-Activity) + * [Suspicious AWS EC2 Activities](#Suspicious-AWS-EC2-Activities) * [Suspicious AWS Login Activities](#Suspicious-AWS-Login-Activities) @@ -1761,6 +1810,8 @@ Various legacy protocols operate by default in the clear, without the protection * [Suspicious AWS Traffic](#Suspicious-AWS-Traffic) +* [Suspicious Cloud Authentication Activities](#Suspicious-Cloud-Authentication-Activities) + * [Unusual AWS EC2 Modifications](#Unusual-AWS-EC2-Modifications) ### AWS Cross Account Activity @@ -1784,6 +1835,7 @@ This Analytic Story includes searches that will help you monitor your AWS CloudT #### Mappings ##### ATT&CK +* T1078 ##### Kill Chain Phases * Actions on Objectives @@ -1823,6 +1875,8 @@ This Analytic Story is focused on detecting suspicious new instances in your EC2 #### Mappings ##### ATT&CK +* T1078 +* T1535 ##### Kill Chain Phases * Actions on Objectives @@ -1904,6 +1958,7 @@ This Analytic Story was designed to provide you with flexibility in the precisio #### Mappings ##### ATT&CK +* T1535 ##### Kill Chain Phases @@ -1942,6 +1997,8 @@ The detection searches in this Analytic Story are designed to help you uncover A #### Mappings ##### ATT&CK +* T1078 +* T1098 ##### Kill Chain Phases * Actions on Objectives @@ -1987,6 +2044,8 @@ This Analytic Story is focused on detecting suspicious new instances in your clo #### Mappings ##### ATT&CK +* T1078 +* T1535 ##### Kill Chain Phases * Actions on Objectives @@ -2024,6 +2083,7 @@ Container Registrys provide a way for organizations to keep customized images of #### Mappings ##### ATT&CK +* T1525 ##### Kill Chain Phases @@ -2049,6 +2109,8 @@ Kubernetes is the most used container orchestration platform, this orchestration * Amazon EKS Kubernetes Pod scan detection * Amazon EKS Kubernetes cluster scan detection * GCP Kubernetes cluster scan detection +* Kubernetes Azure pod scan fingerprint +* Kubernetes Azure scan fingerprint #### Data Models @@ -2066,6 +2128,76 @@ Kubernetes is the most used container orchestration platform, this orchestration ##### References * https://github.com/splunk/cloud-datamodel-security-research +### Kubernetes Sensitive Object Access Activity +* id = 2574e6d9-7254-4751-8925-0447deeec8ea +* date = 2020-05-20 +* version = 1 + +#### Description +This story addresses detection and response of accounts acccesing Kubernetes cluster sensitive objects such as configmaps or secrets providing information on items such as user user, group. object, namespace and authorization reason. + +#### Narrative +Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive objects within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes sensitive objects. + +#### Detections +* AWS EKS Kubernetes cluster sensitive object access +* Kubernetes AWS detect service accounts forbidden failure access +* Kubernetes AWS detect suspicious kubectl calls +* Kubernetes Azure detect sensitive object access +* Kubernetes Azure detect service accounts forbidden failure access +* Kubernetes Azure detect suspicious kubectl calls + +#### Data Models + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Lateral Movement + +###### CIS + +##### NIST + +##### References +* https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html + +### Kubernetes Sensitive Role Activity +* id = 2574e6d9-7254-4751-8925-0447deeec8ew +* date = 2020-05-20 +* version = 1 + +#### Description +This story addresses detection and response around Sensitive Role usage within a Kubernetes clusters against cluster resources and namespaces. + +#### Narrative +Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive roles within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes role activities + +#### Detections +* Kubernetes AWS detect RBAC authorization by account +* Kubernetes AWS detect most active service accounts by pod +* Kubernetes AWS detect sensitive role access +* Kubernetes Azure detect RBAC authorization by account +* Kubernetes Azure detect most active service accounts by pod namespace +* Kubernetes Azure detect sensitive role access + +#### Data Models + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Lateral Movement + +###### CIS + +##### NIST + +##### References +* https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html + ### Suspicious AWS EC2 Activities * id = 2e8948a5-5239-406b-b56b-6c50f1268af3 * date = 2018-02-09 @@ -2090,6 +2222,8 @@ AWS CloudTrail is an AWS service that helps you enable governance, compliance, a #### Mappings ##### ATT&CK +* T1078 +* T1535 ##### Kill Chain Phases * Actions on Objectives @@ -2129,6 +2263,8 @@ It is important to monitor and control who has access to your AWS infrastructure #### Mappings ##### ATT&CK +* T1078 +* T1535 ##### Kill Chain Phases * Actions on Objectives @@ -2166,6 +2302,7 @@ Among things to look out for are S3 access from unfamiliar locations and by unfa #### Mappings ##### ATT&CK +* T1530 ##### Kill Chain Phases * Actions on Objectives @@ -2219,6 +2356,46 @@ The searches in this Analytic Story will monitor your AWS network traffic for ev ##### References * https://rhinosecuritylabs.com/aws/hiding-cloudcobalt-strike-beacon-c2-using-amazon-apis/ +### Suspicious Cloud Authentication Activities +* id = 6380ebbb-55c5-4fce-b754-01fd565fb73c +* date = 2020-06-04 +* version = 1 + +#### Description +Monitor your cloud authentication events. Searches within this Analytic Story leverage the recent cloud updates to the Authentication data model to help you stay aware of and investigate suspicious login activity. + +#### Narrative +It is important to monitor and control who has access to your cloud infrastructure. Detecting suspicious logins 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 compute activity whether legitimate or otherwise.\ +This Analytic Story has data model versions of cloud searches leveraging Authentication data, including those looking for suspicious login activity, and cross-account activity for AWS. + +#### 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 - DM + +#### Data Models +* Authentication + +#### Mappings + +##### ATT&CK +* T1535 + +##### Kill Chain Phases +* Actions on Objectives + +###### CIS +* CIS 16 + +##### NIST +* DE.AE +* DE.DP + +##### References +* https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/ +* https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html + ### Unusual AWS EC2 Modifications * id = 73de57ef-0dfc-411f-b1e7-fa24428aeae0 * date = 2018-04-09 @@ -2239,6 +2416,7 @@ A common attack technique is to infiltrate a cloud instance and make modificatio #### Mappings ##### ATT&CK +* T1078 ##### Kill Chain Phases @@ -2352,7 +2530,6 @@ Suspicious activities--spikes in SMB traffic, processes that launch netsh (to mo #### Mappings ##### ATT&CK -* T1031 * T1043 * T1050 * T1053 @@ -2589,10 +2766,8 @@ This Analytic Story is designed to help you detect and investigate suspicious ac #### Mappings ##### ATT&CK -* T1031 * T1050 * T1059 -* T1089 ##### Kill Chain Phases * Actions on Objectives @@ -2888,9 +3063,8 @@ The Windows operating system uses a services architecture to allow for running c #### Mappings ##### ATT&CK -* T1031 * T1050 -* T1089 +* T1058 ##### Kill Chain Phases * Actions on Objectives diff --git a/lookups/mitre_enrichment.csv b/lookups/mitre_enrichment.csv new file mode 100644 index 0000000000..fecfaed157 --- /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 +T1221,Template Injection,Defense Evasion,APT28|Tropic Trooper|Dragonfly 2.0|DarkHydrus +T1220,XSL Script Processing,Defense Evasion|Execution,Cobalt Group +T1197,BITS Jobs,Defense Evasion|Persistence,Leviathan +T1217,Browser Bookmark Discovery,Discovery,no +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 +T1211,Exploitation for Defense Evasion,Defense Evasion,APT28 +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 +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|Elderwood|TA459 +T1208,Kerberoasting,Credential Access,no +T1215,Kernel Modules and Extensions,Persistence,no +T1202,Indirect Command Execution,Defense Evasion,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 +T1194,Spearphishing via Service,Initial Access,FIN6|OilRig|Dark Caracal|Magic Hound +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|APT19|OilRig|BRONZE BUTLER|FIN7|Dragonfly 2.0|APT32|FIN8|MuddyWater|APT28|TA459|Elderwood|APT29|menuPass|APT37|Patchwork|Leviathan|Magic Hound|PLATINUM +T1195,Supply Chain Compromise,Initial Access,APT41|Elderwood +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|BRONZE BUTLER|APT19|Dark Caracal|Cobalt Group|FIN7|Turla|OilRig|DarkHydrus|MuddyWater|Gorgon Group|Patchwork|Rancor|Lazarus Group|APT32|APT37|APT28|APT29|FIN8|menuPass|Leviathan|Elderwood|TA459|Magic Hound|PLATINUM +T1192,Spearphishing Link,Initial Access,Machete|Kimsuky|TA505|Stolen Pencil|APT39|FIN4|APT32|Night Dragon|APT28|Turla|Cobalt Group|OilRig|Dragonfly 2.0|APT33|Elderwood|APT29|Leviathan|Magic Hound|FIN8|Patchwork +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 +T1172,Domain Fronting,Command And Control,APT29 +T1173,Dynamic Data Exchange,Execution,TA505|MuddyWater|Gallmaker|Patchwork|Cobalt Group|APT37|APT28|FIN7 +T1181,Extra Window Memory Injection,Defense Evasion|Privilege Escalation,no +T1179,Hooking,Persistence|Privilege Escalation|Credential Access,PLATINUM +T1187,Forced Authentication,Credential Access,DarkHydrus|Dragonfly 2.0 +T1177,LSASS Driver,Execution|Persistence,no +T1171,LLMNR/NBT-NS Poisoning and Relay,Credential Access,no +T1188,Multi-hop Proxy,Command And Control,FIN4|APT29 +T1170,Mshta,Defense Evasion|Execution,Kimsuky|APT32|MuddyWater|FIN7 +T1183,Image File Execution Options Injection,Privilege Escalation|Persistence|Defense Evasion,TEMP.Veles +T1185,Man in the Browser,Collection,no +T1174,Password Filter DLL,Credential Access,no +T1184,SSH Hijacking,Lateral Movement,no +T1180,Screensaver,Persistence,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 +T1146,Clear Command History,Defense Evasion,APT41 +T1140,Deobfuscate/Decode Files or Information,Defense Evasion,Turla|WIRTE|Darkhotel|Tropic Trooper|Honeybee|menuPass|Gorgon Group|Threat Group-3390|APT19|Leviathan|MuddyWater|APT28|OilRig|BRONZE BUTLER +T1157,Dylib Hijacking,Persistence|Privilege Escalation,no +T1143,Hidden Window,Defense Evasion,Gorgon Group|Deep Panda|DarkHydrus|CopyKittens|APT19|APT32|APT28|APT3|Magic Hound +T1139,Bash History,Credential Access,no +T1148,HISTCONTROL,Defense Evasion,no +T1147,Hidden Users,Defense Evasion,no +T1136,Create Account,Persistence,APT41|Soft Cell|Dragonfly 2.0|Leafminer|APT3 +T1141,Input Prompt,Credential Access,FIN4 +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 +T1162,Login Item,Persistence,no +T1168,Local Job Scheduling,Persistence|Execution,no +T1137,Office Application Startup,Persistence,APT32|APT28 +T1142,Keychain,Credential Access,no +T1159,Launch Agent,Persistence,no +T1135,Network Share Discovery,Discovery,APT41|Tropic Trooper|APT1|Dragonfly 2.0|Sowbug +T1161,LC_LOAD_DYLIB Addition,Persistence,no +T1160,Launch Daemon,Persistence|Privilege Escalation,no +T1150,Plist Modification,Defense Evasion|Persistence|Privilege Escalation,no +T1145,Private Keys,Credential Access,no +T1163,Rc.common,Persistence,no +T1151,Space after Filename,Defense Evasion|Execution,no +T1153,Source,Execution,no +T1164,Re-opened Applications,Persistence,no +T1169,Sudo,Privilege Escalation,no +T1154,Trap,Execution|Persistence,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|APT28|Dragonfly 2.0|Magic Hound|Ke3chang|Leafminer|APT1 +T1113,Screen Capture,Collection,Silence|MuddyWater|OilRig|Dragonfly 2.0|FIN7|Dark Caracal|BRONZE BUTLER|Magic Hound|Group5|APT28 +T1112,Modify Registry,Defense Evasion,APT41|Turla|APT32|APT38|Dragonfly 2.0|Threat Group-3390|APT19|Patchwork|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|Cobalt Group|menuPass|Dragonfly 2.0|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|Rancor|Cobalt Group|Gorgon Group|Turla|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|Cobalt Group|Threat Group-3390|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|Cobalt Group|APT19|Gorgon Group|Thrip|APT28|Dragonfly 2.0|Leviathan|MuddyWater|TA459|FIN8|CopyKittens|BRONZE BUTLER|OilRig|Magic Hound|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|BRONZE BUTLER|Sowbug|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|OilRig|Magic Hound|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|APT32|FIN10|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|FIN10|menuPass|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|APT37|Ke3chang|Turla|Rancor|Honeybee|Orangeworm|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|Ke3chang|Dragonfly 2.0|Patchwork|Leafminer|Rancor|FIN7|Honeybee|Cobalt Group|APT37|Gorgon Group|MuddyWater|Leviathan|FIN8|TA459|APT28|Magic Hound|OilRig|BRONZE BUTLER|FIN5|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|Dragonfly 2.0|Gorgon Group|Cobalt Group|Honeybee|Threat Group-3390|Dark Caracal|Ke3chang|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|Threat Group-3390|menuPass|Patchwork|Suckfly|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|Cobalt Group|APT37|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|Patchwork|OilRig|Rancor|FIN8|BRONZE BUTLER|menuPass|FIN10|FIN7|APT32|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|Threat Group-3390|APT32|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|Cobalt Group|Leafminer|APT37|Threat Group-3390|Honeybee|Dark Caracal|APT19|FIN8|BlackOasis|Leviathan|Elderwood|MuddyWater|FIN7|APT3|Magic Hound|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|FIN5|APT3|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|APT28|Magic Hound|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/lookups/previously_seen_running_windows_services.csv b/lookups/previously_seen_running_windows_services.csv index bbf1b909d5..e69de29bb2 100644 --- a/lookups/previously_seen_running_windows_services.csv +++ b/lookups/previously_seen_running_windows_services.csv @@ -1 +0,0 @@ -firstTime,lastTime,serviceName diff --git a/lookups/previously_seen_running_windows_services.yml b/lookups/previously_seen_running_windows_services.yml index 1c20ffd82d..7924c34894 100644 --- a/lookups/previously_seen_running_windows_services.yml +++ b/lookups/previously_seen_running_windows_services.yml @@ -1,3 +1,4 @@ description: A placeholder for the list of Windows Services running -filename: previously_seen_running_windows_services.csv +collection: previously_seen_running_windows_services name: previously_seen_running_windows_services +fields_list: _key, service, firstTimeSeen, lastTimeSeen diff --git a/lookups/prohibited_apps_launching_cmd.csv b/lookups/prohibited_apps_launching_cmd.csv index 01edda483b..5e4a2cf651 100644 --- a/lookups/prohibited_apps_launching_cmd.csv +++ b/lookups/prohibited_apps_launching_cmd.csv @@ -14,3 +14,4 @@ firefox.exe,prohibited java.exe,prohibited powershell.exe,prohibited mshta.exe, prohibited +zoom.exe,prohibitied diff --git a/lookups/zoom_first_time_child_process.yml b/lookups/zoom_first_time_child_process.yml new file mode 100644 index 0000000000..a55dc842f0 --- /dev/null +++ b/lookups/zoom_first_time_child_process.yml @@ -0,0 +1,4 @@ +description: A list of suspicious file names +collection: zoom_first_time_child_process +name: zoom_first_time_child_process +fields_list: _key, dest, process_name, firstTimeSeen, lastTimeSeen diff --git a/macros/aws_cross_account_activity_from_previously_unseen_account___dm_filter.yml b/macros/aws_cross_account_activity_from_previously_unseen_account___dm_filter.yml new file mode 100644 index 0000000000..e5d34bcc57 --- /dev/null +++ b/macros/aws_cross_account_activity_from_previously_unseen_account___dm_filter.yml @@ -0,0 +1,3 @@ +definition: search * +description: Use this macro to add additional filters +name: aws_cross_account_activity_from_previously_unseen_account___dm_filter diff --git a/macros/kubernetes_aws_detect_sensitive_object_access.yml b/macros/detect_new_user_aws_console_login___dm_filter.yml similarity index 59% rename from macros/kubernetes_aws_detect_sensitive_object_access.yml rename to macros/detect_new_user_aws_console_login___dm_filter.yml index b6589f0b73..9b3b26607a 100644 --- a/macros/kubernetes_aws_detect_sensitive_object_access.yml +++ b/macros/detect_new_user_aws_console_login___dm_filter.yml @@ -1,3 +1,3 @@ definition: search * description: Use this macro to add additional filters -name: kubernetes_aws_detect_sensitive_object_access +name: detect_new_user_aws_console_login___dm_filter diff --git a/macros/kubernetes_aws_detect_most_active_service_accounts_by_pod.yml b/macros/kubernetes_aws_detect_most_active_service_accounts_by_pod.yml deleted file mode 100644 index cf8f17ae8e..0000000000 --- a/macros/kubernetes_aws_detect_most_active_service_accounts_by_pod.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters -name: kubernetes_aws_detect_most_active_service_accounts_by_pod diff --git a/macros/kubernetes_aws_detect_sensitive_role_access.yml b/macros/kubernetes_aws_detect_sensitive_role_access.yml deleted file mode 100644 index d2fd59afb9..0000000000 --- a/macros/kubernetes_aws_detect_sensitive_role_access.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters -name: kubernetes_aws_detect_sensitive_role_access diff --git a/macros/kubernetes_aws_detect_service_accounts_forbidden_failure_access.yml b/macros/kubernetes_aws_detect_service_accounts_forbidden_failure_access.yml deleted file mode 100644 index 2295e0e1a9..0000000000 --- a/macros/kubernetes_aws_detect_service_accounts_forbidden_failure_access.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters -name: kubernetes_aws_detect_service_accounts_forbidden_failure_access diff --git a/macros/kubernetes_aws_detect_suspicious_kubectl_calls.yml b/macros/kubernetes_aws_detect_suspicious_kubectl_calls.yml deleted file mode 100644 index 04625bbd7f..0000000000 --- a/macros/kubernetes_aws_detect_suspicious_kubectl_calls.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters -name: kubernetes_aws_detect_suspicious_kubectl_calls diff --git a/macros/kubernetes_azure.yml b/macros/kubernetes_azure.yml new file mode 100644 index 0000000000..2a23d362e5 --- /dev/null +++ b/macros/kubernetes_azure.yml @@ -0,0 +1,3 @@ +definition: sourcetype=mscs:storage:blob:json +description: customer specific splunk configurations(eg- index, source, sourcetype) for Kubernetes data from Azure. Replace the macro definition with configurations for your Splunk Environmnent. +name: kubernetes_azure diff --git a/macros/kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace.yml b/macros/kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace.yml index 27e7a530a7..86245d47a6 100644 --- a/macros/kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace.yml +++ b/macros/kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace.yml @@ -1,3 +1,3 @@ definition: search * description: Use this macro to add additional filters -name: kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace +name: kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace_filter diff --git a/macros/kubernetes_aws_detect_RBAC_authorization_by_account.yml b/macros/kubernetes_azure_detect_rbac_authorization_by_account.yml similarity index 52% rename from macros/kubernetes_aws_detect_RBAC_authorization_by_account.yml rename to macros/kubernetes_azure_detect_rbac_authorization_by_account.yml index fc80856b1a..964e8e2e04 100644 --- a/macros/kubernetes_aws_detect_RBAC_authorization_by_account.yml +++ b/macros/kubernetes_azure_detect_rbac_authorization_by_account.yml @@ -1,3 +1,3 @@ definition: search * description: Use this macro to add additional filters -name: kubernetes_aws_detect_RBAC_authorization_by_account +name: kubernetes_azure_detect_rbac_authorization_by_account_filter diff --git a/macros/kubernetes_azure_detect_sensitive_object_access.yml b/macros/kubernetes_azure_detect_sensitive_object_access.yml index 8fac13d818..dce2bd7be8 100644 --- a/macros/kubernetes_azure_detect_sensitive_object_access.yml +++ b/macros/kubernetes_azure_detect_sensitive_object_access.yml @@ -1,3 +1,3 @@ definition: search * description: Use this macro to add additional filters -name: kubernetes_azure_detect_sensitive_object_access +name: kubernetes_azure_detect_sensitive_object_access_filter diff --git a/macros/kubernetes_azure_detect_sensitive_role_access.yml b/macros/kubernetes_azure_detect_sensitive_role_access.yml index 612dfd2272..54bf94d705 100644 --- a/macros/kubernetes_azure_detect_sensitive_role_access.yml +++ b/macros/kubernetes_azure_detect_sensitive_role_access.yml @@ -1,3 +1,3 @@ definition: search * description: Use this macro to add additional filters -name: kubernetes_azure_detect_sensitive_role_access +name: kubernetes_azure_detect_sensitive_role_access_filter diff --git a/macros/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml b/macros/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml index 9cef6c37b8..0483b01baa 100644 --- a/macros/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml +++ b/macros/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml @@ -1,3 +1,3 @@ definition: search * description: Use this macro to add additional filters -name: kubernetes_azure_detect_service_accounts_forbidden_failure_access +name: kubernetes_azure_detect_service_accounts_forbidden_failure_access_filter diff --git a/macros/kubernetes_azure_detect_suspicious_kubectl_calls.yml b/macros/kubernetes_azure_detect_suspicious_kubectl_calls.yml index ac101e0a94..9f57d71281 100644 --- a/macros/kubernetes_azure_detect_suspicious_kubectl_calls.yml +++ b/macros/kubernetes_azure_detect_suspicious_kubectl_calls.yml @@ -1,3 +1,3 @@ definition: search * description: Use this macro to add additional filters -name: kubernetes_azure_detect_suspicious_kubectl_calls +name: kubernetes_azure_detect_suspicious_kubectl_calls_filter diff --git a/macros/kubernetes_azure_pod_scan_fingerprint_detection_filter.yml b/macros/kubernetes_azure_pod_scan_fingerprint_detection_filter.yml index a1e8b5991e..0b2ca96cbb 100644 --- a/macros/kubernetes_azure_pod_scan_fingerprint_detection_filter.yml +++ b/macros/kubernetes_azure_pod_scan_fingerprint_detection_filter.yml @@ -1,3 +1,3 @@ definition: search * description: Use this macro to add additional filters -name: kubernetes_azure_pod_scan_fingerprint_detection +name: kubernetes_azure_pod_scan_fingerprint_detection_filter diff --git a/macros/kubernetes_azure_scan_fingerprint_detection_filter.yml b/macros/kubernetes_azure_scan_fingerprint_filter.yml similarity index 60% rename from macros/kubernetes_azure_scan_fingerprint_detection_filter.yml rename to macros/kubernetes_azure_scan_fingerprint_filter.yml index 1c98d00ea1..caed65bae0 100644 --- a/macros/kubernetes_azure_scan_fingerprint_detection_filter.yml +++ b/macros/kubernetes_azure_scan_fingerprint_filter.yml @@ -1,3 +1,3 @@ definition: search * description: Use this macro to add additional filters -name: kubernetes_azure_scan_fingerprint_detection +name: kubernetes_azure_scan_fingerprint_filter diff --git a/macros/previously_seen_windows_services_forget_window.yml b/macros/previously_seen_windows_services_forget_window.yml new file mode 100644 index 0000000000..84cf3b446f --- /dev/null +++ b/macros/previously_seen_windows_services_forget_window.yml @@ -0,0 +1,3 @@ +description: Use this macro to determine how long to keep track of Windows services +definition: -90d@d +name: previously_seen_windows_services_forget_window diff --git a/macros/previously_seen_windows_services_window.yml b/macros/previously_seen_windows_services_window.yml new file mode 100644 index 0000000000..f67200887b --- /dev/null +++ b/macros/previously_seen_windows_services_window.yml @@ -0,0 +1,3 @@ +description: Use this macro to determine how far back you should be checking for new Windows services +definition: -70m@m +name: previously_seen_windows_service_window diff --git a/macros/previously_seen_zoom_child_processes_forget_window.yml b/macros/previously_seen_zoom_child_processes_forget_window.yml new file mode 100644 index 0000000000..59896edb25 --- /dev/null +++ b/macros/previously_seen_zoom_child_processes_forget_window.yml @@ -0,0 +1,3 @@ +description: Use this macro to determine how long to keep track of zoom child processes +definition: -90d@d +name: previously_seen_zoom_child_processes_forget_window \ No newline at end of file diff --git a/macros/previously_seen_zoom_child_processes_window.yml b/macros/previously_seen_zoom_child_processes_window.yml new file mode 100644 index 0000000000..4c7dfeab40 --- /dev/null +++ b/macros/previously_seen_zoom_child_processes_window.yml @@ -0,0 +1,3 @@ +description: Use this macro to determine how far back you should be checking for new zoom child processes +definition: -70m@m +name: previously_seen_zoom_child_processes_window diff --git a/macros/security_content_summariesonly.yml b/macros/security_content_summariesonly.yml index 15b9d14bb7..837760af99 100644 --- a/macros/security_content_summariesonly.yml +++ b/macros/security_content_summariesonly.yml @@ -1,3 +1,3 @@ -definition: summariesonly=true allow_old_summaries=true +definition: summariesonly=false allow_old_summaries=true description: search data model's summaries only name: security_content_summariesonly diff --git a/package/app.manifest b/package/app.manifest index b8cecfff26..d0f392cc90 100644 --- a/package/app.manifest +++ b/package/app.manifest @@ -5,7 +5,7 @@ "id": { "group": null, "name": "DA-ESS-ContentUpdate", - "version": "1.0.54" + "version": "3.0.3" }, "author": [ { diff --git a/package/appserver/static/analytic_story_details.js b/package/appserver/static/analytic_story_details.js index 113c93d45c..932b4426f0 100644 --- a/package/appserver/static/analytic_story_details.js +++ b/package/appserver/static/analytic_story_details.js @@ -139,7 +139,7 @@ require([ $('.run-story').unbind('click'); $('.run-story').on('click', function(evt) { - window.open('https://github.com/splunk/analytic_story_execution'); + window.open('/en-US/app/Splunk_ASX/execute?form.mode=now&form.cron=*%2F15%20*%20*%20*%20*&form.earliest_time=-15m&form.latest_time=now&form.time.earliest=-24h%40h&form.time.latest=now&form.story=' + asName); }); let asSearch = splunkjs.mvc.Components.getInstance(epoch); @@ -572,4 +572,4 @@ require([ return htmlTmpl; } -}); \ No newline at end of file +}); diff --git a/package/default/analytic_stories.conf b/package/default/analytic_stories.conf index 492b5aa4ab..7d114d6b3f 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: 2020-05-14T08:00:44 UTC +# On Date: 2020-06-26T13:32:24 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# @@ -15,8 +15,8 @@ id = 2f2f610a-d64d-48c2-b57c-967a2b49ab5a 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"], "nist": ["DE.AE"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - AWS Investigate User Activities By AccessKeyId", "ESCU - AWS Investigate User Activities By Source User"] +mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "nist": ["DE.AE"]} +investigative_searches = ["ESCU - AWS Investigate User Activities By AccessKeyId", "ESCU - AWS Investigate User Activities By Source User", "ESCU - Get Notable History"] support_searches = ["ESCU - Previously Seen AWS Cross Account Activity"] data_models = [] providing_technologies = none @@ -33,9 +33,9 @@ id = ced74200-8465-4bc3-bd2c-9a782eec6750 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"], "nist": ["DE.AE", "DE.DP", "ID.AM"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Notable Info", "ESCU - Investigate AWS activities via region name", "ESCU - Get EC2 Launch Details"] -support_searches = ["ESCU - Previously Seen EC2 AMIs", "ESCU - Previously Seen EC2 Launches By User", "ESCU - Previously Seen EC2 Instance Types", "ESCU - Previously Seen AWS Regions"] +mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078", "T1535"], "nist": ["DE.AE", "DE.DP", "ID.AM"]} +investigative_searches = ["ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Launch Details", "ESCU - Get User Information from Identity Table", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable History"] +support_searches = ["ESCU - Previously Seen EC2 AMIs", "ESCU - Previously Seen EC2 Instance Types", "ESCU - Previously Seen EC2 Launches By User", "ESCU - Previously Seen AWS Regions"] data_models = [] 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. @@ -53,8 +53,8 @@ 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"], "nist": ["DE.AE", "DE.CM", "DE.DP", "PR.AC"]} -investigative_searches = ["ESCU - AWS Network ACL Details from ID", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio"] -support_searches = ["ESCU - Baseline of Network ACL Activity by ARN", "ESCU - Baseline of blocked outbound traffic from AWS"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Risk Modifiers For User", "ESCU - Get All AWS Activity From IP Address", "ESCU - AWS Network ACL Details from ID", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get User Information from Identity Table", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] +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. @@ -68,8 +68,8 @@ id = 3338b567-3804-4261-9889-cf0ca4753c7f 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"], "nist": ["ID.AM"]} -investigative_searches = ["ESCU - Get All AWS Activity From City", "ESCU - Get All AWS Activity From Region", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get All AWS Activity From Country"] +mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1535"], "nist": ["ID.AM"]} +investigative_searches = ["ESCU - Get All AWS Activity From City", "ESCU - Get All AWS Activity From Country", "ESCU - AWS Investigate Security Hub alerts by dest", "ESCU - Get All AWS Activity From Region", "ESCU - Get All AWS Activity From IP Address"] support_searches = ["ESCU - Previously Seen AWS Provisioning Activity Sources"] data_models = [] providing_technologies = none @@ -85,9 +85,9 @@ id = 2e8948a5-5239-406b-b56b-6c50f1269af3 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"], "nist": ["DE.CM", "DE.DP", "ID.AM", "PR.AC"]} -investigative_searches = ["ESCU - Investigate AWS User Activities by user field", "ESCU - Get Notable History", "ESCU - Get Notable Info"] -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"], "mitre_attack": ["T1078", "T1098"], "nist": ["DE.CM", "DE.DP", "ID.AM", "PR.AC"]} +investigative_searches = ["ESCU - Investigate AWS User Activities by user field", "ESCU - Get Notable Info", "ESCU - Get Notable History"] +support_searches = ["ESCU - Previously seen API call per user roles in CloudTrail", "ESCU - Create a list of approved AWS service accounts", "ESCU - Baseline of Security Group Activity by ARN", "ESCU - Baseline of API Calls per User ARN"] data_models = [] 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. @@ -102,10 +102,10 @@ creation_date = 2017-09-06 modification_date = 2017-09-06 id = 8892a655-6205-55f7-abba-06460e38c8ae version = 1 -reference = ["https://www.sans.org/media/critical-security-controls/critical-controls-poster-2016.pdf"] +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"], "mitre_attack": ["T1078", "T1136"], "nist": ["PR.IP"]} -investigative_searches = ["ESCU - Get Logon Rights Modifications For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Logon Rights Modifications For User"] +investigative_searches = ["ESCU - Get Logon Rights Modifications For User", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Logon Rights Modifications For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Change"] providing_technologies = none @@ -121,7 +121,7 @@ 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"], "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 Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web POSTs From src", "ESCU - Investigate Suspicious Strings in HTTP Header", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Investigate Suspicious Strings in HTTP Header", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Investigate Web POSTs From src", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -150,7 +150,7 @@ 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"], "nist": ["PR.DS"]} -investigative_searches = ["ESCU - Get First Occurrence and Last Occurrence of a MAC Address", "ESCU - Get Notable History", "ESCU - Get Notable Info"] +investigative_searches = ["ESCU - Get First Occurrence and Last Occurrence of a MAC Address", "ESCU - Get Notable Info", "ESCU - Get Notable History"] support_searches = ["ESCU - Count of assets by category"] data_models = ["Network_Sessions"] providing_technologies = none @@ -166,7 +166,7 @@ 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"], "nist": ["PR.IP"]} -investigative_searches = ["ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get User Information from Identity Table", "ESCU - Get Emails From Specific Sender", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Email Info"] +investigative_searches = ["ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get User Information from Identity Table", "ESCU - Get Email Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Emails From Specific Sender", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = ["ESCU - DNSTwist Domain Names"] data_models = ["Email", "Network_Resolution", "Web"] providing_technologies = none @@ -183,9 +183,9 @@ id = 3b96d13c-fdc7-45dd-b3ad-c132b31cdd2a 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"], "nist": ["DE.AE", "DE.DP", "ID.AM"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Investigate User Activities In All Cloud Regions", "ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate Cloud Compute Instance Activities", "ESCU - Investigate User Activities In Single Cloud Region", "ESCU - Get Notable Info", "ESCU - Investigate AWS activities via region name", "ESCU - Get EC2 Launch Details"] -support_searches = ["ESCU - Previously Seen Cloud Compute Instance Types", "ESCU - Previously Seen Cloud Compute Creations By User", "ESCU - Previously Seen Cloud Compute Images", "ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK", "ESCU - Previously Seen Cloud Regions"] +mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078", "T1535"], "nist": ["DE.AE", "DE.DP", "ID.AM"]} +investigative_searches = ["ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Launch Details", "ESCU - Investigate User Activities In Single Cloud Region", "ESCU - AWS Investigate Security Hub alerts by dest", "ESCU - Investigate User Activities In All Cloud Regions", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Cloud Compute Instance Activities", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable History"] +support_searches = ["ESCU - Previously Seen Cloud Compute Creations By User", "ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK", "ESCU - Previously Seen Cloud Compute Images", "ESCU - Previously Seen Cloud Compute Instance Types", "ESCU - Previously Seen Cloud Regions"] data_models = ["Cloud_Infrastructure"] 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. @@ -203,7 +203,7 @@ 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"], "nist": ["DE.DP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From src ip"] +investigative_searches = ["ESCU - Investigate Network Traffic From src ip", "ESCU - Get Risk Modifiers For User", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From src ip", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = [] providing_technologies = none @@ -221,7 +221,7 @@ 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": ["T1043", "T1074", "T1114"], "nist": ["DE.AE", "DE.CM", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Endpoint", "Network_Traffic"] providing_technologies = none @@ -239,8 +239,8 @@ 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": ["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 - AWS Network Interface details via resourceId", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Parent Process Info"] -support_searches = ["ESCU - Baseline of DNS Query Length - MLTK", "ESCU - Baseline of blocked outbound traffic from AWS"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get All AWS Activity From IP Address", "ESCU - AWS Network ACL Details from ID", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get User Information from Identity Table", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] +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. @@ -272,7 +272,7 @@ id = aa0e28b1-0521-4b6f-9d2a-7b87e34af246 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 = {} +mappings = {"mitre_attack": ["T1525"]} investigative_searches = ["ESCU - Investigate AWS ECR container listing activity"] support_searches = [] data_models = [] @@ -289,7 +289,7 @@ 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": ["T1003", "T1064", "T1086"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.IP"]} -investigative_searches = ["ESCU - Investigate Pass the Hash Attempts", "ESCU - Investigate Failed Logins for Multiple Destinations", "ESCU - Investigate Previous Unseen User", "ESCU - Investigate Pass the Ticket Attempts"] +investigative_searches = ["ESCU - Investigate Previous Unseen User", "ESCU - Investigate Pass the Hash Attempts", "ESCU - Investigate Failed Logins for Multiple Destinations", "ESCU - Investigate Pass the Ticket Attempts"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -306,9 +306,9 @@ id = 0c016e5c-88be-4e2c-8c6c-c2b55b4fb4ef 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": ["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 Process Registry Activity", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Information For Port Activity", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Process File Activity"] -support_searches = ["ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"] +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": ["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 Process Info", "ESCU - Get Registry Activities", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Registry Activity", "ESCU - Get Parent Process Info", "ESCU - Get Process File Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] +support_searches = ["ESCU - Previously seen command line arguments", "ESCU - Baseline of SMB Traffic - MLTK"] 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. @@ -326,7 +326,7 @@ 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 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.IP"]} -investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Network_Resolution"] providing_technologies = none @@ -367,7 +367,7 @@ 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"], "mitre_attack": ["T1043"], "nist": ["DE.AE", "DE.CM", "PR.DS", "PR.PT"]} -investigative_searches = ["ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get User Information from Identity Table", "ESCU - Get DNS Server History for a host", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Change_Analysis", "Network_Resolution"] providing_technologies = none @@ -382,9 +382,9 @@ id = fcc27099-46a0-46b0-a271-5c7dab56b6f1 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": ["T1031", "T1050", "T1059", "T1089", "T1112"], "nist": ["DE.CM", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] -support_searches = ["ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"] +mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Installation"], "mitre_attack": ["T1050", "T1059", "T1089", "T1112"], "nist": ["DE.CM", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] +support_searches = ["ESCU - Previously seen command line arguments", "ESCU - Baseline of SMB Traffic - MLTK"] 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. @@ -399,7 +399,7 @@ 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 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 Process Responsible For The DNS Traffic", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Investigate Web Activity From src ip"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From src ip", "ESCU - Get DNS Server History for a host", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Network_Resolution", "Web"] providing_technologies = none @@ -415,7 +415,7 @@ 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": ["T1043", "T1059", "T1072", "T1087", "T1103", "T1131"], "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 - Investigate Web Activity From Host", "ESCU - Get Process Information For Port Activity", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Parent Process Info"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = ["ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Add Prohibited Processes to Enterprise Security"] data_models = ["Email", "Endpoint", "Network_Traffic"] providing_technologies = none @@ -433,8 +433,8 @@ 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": ["T1043", "T1059", "T1064", "T1076"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get DNS traffic ratio", "ESCU - Get Parent Process Info"] -support_searches = ["ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Baseline of DNS Query Length - MLTK", "ESCU - Previously seen command line arguments"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Parent Process Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] +support_searches = ["ESCU - Previously seen command line arguments", "ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Baseline of DNS Query Length - MLTK"] 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. @@ -452,7 +452,7 @@ 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"], "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 Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get DNS Server History for a host", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Network_Resolution"] providing_technologies = none @@ -468,7 +468,7 @@ 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 18"], "kill_chain_phases": ["Delivery", "Reconnaissance"], "mitre_attack": ["T1082"], "nist": ["DE.CM"]} -investigative_searches = ["ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Vulnerability Logs For Endpoint"] +investigative_searches = ["ESCU - Investigate Web Activity From Host", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Web"] providing_technologies = none @@ -496,25 +496,57 @@ modification_date = 2020-04-15 id = a9ef59cf-e981-4e66-9eef-bb049f695c09 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"] +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", "ESCU - Kubernetes Azure pod scan fingerprint - Rule", "ESCU - Kubernetes Azure scan fingerprint - Rule"] mappings = {"kill_chain_phases": ["Reconnaissance"]} -investigative_searches = ["ESCU - GCP Kubernetes activity by src ip", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Amazon EKS Kubernetes activity by src ip", "ESCU - Get Notable Info"] +investigative_searches = ["ESCU - Amazon EKS Kubernetes activity by src ip", "ESCU - GCP Kubernetes activity by src ip", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] 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. +[Kubernetes Sensitive Object Access Activity] +category = Cloud Security +creation_date = 2020-05-20 +modification_date = 2020-05-20 +id = 2574e6d9-7254-4751-8925-0447deeec8ea +version = 1 +reference = ["https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html"] +detection_searches = ["ESCU - AWS EKS Kubernetes cluster sensitive object access - Rule", "ESCU - Kubernetes AWS detect service accounts forbidden failure access - Rule", "ESCU - Kubernetes AWS detect suspicious kubectl calls - Rule", "ESCU - Kubernetes Azure detect sensitive object access - Rule", "ESCU - Kubernetes Azure detect service accounts forbidden failure access - Rule", "ESCU - Kubernetes Azure detect suspicious kubectl calls - Rule"] +mappings = {"kill_chain_phases": ["Lateral Movement"]} +investigative_searches = ["ESCU - Get Notable Info"] +support_searches = [] +data_models = [] +providing_technologies = none +description = This story addresses detection and response of accounts acccesing Kubernetes cluster sensitive objects such as configmaps or secrets providing information on items such as user user, group. object, namespace and authorization reason. +narrative = Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive objects within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes sensitive objects. + +[Kubernetes Sensitive Role Activity] +category = Cloud Security +creation_date = 2020-05-20 +modification_date = 2020-05-20 +id = 2574e6d9-7254-4751-8925-0447deeec8ew +version = 1 +reference = ["https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html"] +detection_searches = ["ESCU - Kubernetes AWS detect RBAC authorization by account - Rule", "ESCU - Kubernetes AWS detect most active service accounts by pod - Rule", "ESCU - Kubernetes AWS detect sensitive role access - Rule", "ESCU - Kubernetes Azure detect RBAC authorization by account - Rule", "ESCU - Kubernetes Azure detect most active service accounts by pod namespace - Rule", "ESCU - Kubernetes Azure detect sensitive role access - Rule"] +mappings = {"kill_chain_phases": ["Lateral Movement"]} +investigative_searches = ["ESCU - Get Notable Info"] +support_searches = [] +data_models = [] +providing_technologies = none +description = This story addresses detection and response around Sensitive Role usage within a Kubernetes clusters against cluster resources and namespaces. +narrative = Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive roles within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes role activities + [Lateral Movement] category = Adversary Tactics creation_date = 2020-02-04 modification_date = 2020-02-04 id = 399d65dc-1f08-499b-a259-aad9051f38ad version = 2 -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"] -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 9"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053", "T1075", "T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +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 - Kerberoasting spn request with RC4 encryption - 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 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053", "T1075", "T1076", "T1208"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.IP"]} +investigative_searches = ["ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Endpoint", "Network_Traffic"] providing_technologies = none @@ -534,7 +566,7 @@ 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"], "mitre_attack": ["T1064", "T1086"], "nist": ["DE.CM", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -559,7 +591,7 @@ 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"], "nist": ["PR.IP"]} -investigative_searches = ["ESCU - All backup logs for host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - All backup logs for host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = ["ESCU - Monitor Successful Backups", "ESCU - Monitor Unsuccessful Backups"] data_models = [] providing_technologies = none @@ -575,7 +607,7 @@ 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"], "nist": ["PR.DS"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Parent Process Info"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = ["ESCU - Add Prohibited Processes to Enterprise Security"] data_models = ["Endpoint"] providing_technologies = none @@ -592,7 +624,7 @@ 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"], "nist": ["PR.MA"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Updates"] providing_technologies = none @@ -607,11 +639,11 @@ creation_date = 2017-01-05 modification_date = 2017-01-05 id = 2b1800dd-92f9-47ec-a981-fdf1351e5f65 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"] +reference = ["https://docs.microsoft.com/en-us/previous-versions/tn-archive/bb490939(v=technet.10)", "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": ["T1059", "T1089"], "nist": ["DE.CM", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] -support_searches = ["ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] +support_searches = ["ESCU - Previously seen command line arguments", "ESCU - Baseline of SMB Traffic - MLTK"] 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. @@ -626,9 +658,9 @@ id = bb9f5ed2-916e-4364-bb6d-97c370efcf52 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 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 Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] -support_searches = ["ESCU - Previously Seen Running Windows Services", "ESCU - Previously seen command line arguments"] +mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Installation"], "mitre_attack": ["T1050", "T1059", "T1064"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] +support_searches = ["ESCU - Previously seen command line arguments", "ESCU - Previously Seen Running Windows Services - Update", "ESCU - Previously Seen Running Windows Services - Initial"] 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. @@ -670,8 +702,8 @@ 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": ["T1059", "T1064", "T1086", "T1103", "T1131"], "nist": ["DE.AE", "DE.CM", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] -support_searches = ["ESCU - Baseline of Command Line Length - MLTK", "ESCU - Previously seen command line arguments"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] +support_searches = ["ESCU - Previously seen command line arguments", "ESCU - Baseline of Command Line Length - MLTK"] data_models = ["Endpoint"] providing_technologies = none description = Monitor your environment for suspicious behaviors that resemble the techniques employed by the MUDCARP threat group. @@ -713,7 +745,7 @@ 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 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 Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Parent Process Info"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get DNS Server History for a host", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Network_Resolution", "Network_Traffic"] providing_technologies = none @@ -729,7 +761,7 @@ 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": ["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 Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Information For Port Activity", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Parent Process Info"] +investigative_searches = ["ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = ["ESCU - Baseline of Command Line Length - MLTK", "ESCU - Baseline of SMB Traffic - MLTK"] data_models = ["Endpoint", "Network_Traffic"] providing_technologies = none @@ -745,7 +777,7 @@ 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"], "nist": ["PR.IP"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Authentication"] providing_technologies = none @@ -762,7 +794,7 @@ 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 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"] +investigative_searches = ["ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Web"] providing_technologies = none @@ -779,7 +811,7 @@ 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": ["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 - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Parent Process Info"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = ["ESCU - Add Prohibited Processes to Enterprise Security"] data_models = ["Endpoint", "Network_Traffic", "Web"] providing_technologies = none @@ -800,7 +832,7 @@ version = 1 reference = ["https://meltdownattack.com/"] detection_searches = ["ESCU - Spectre and Meltdown Vulnerable Systems - Rule"] mappings = {"cis20": ["CIS 4"], "nist": ["DE.CM"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = ["ESCU - Systems Ready for Spectre-Meltdown Windows Patch"] data_models = ["Vulnerabilities"] providing_technologies = none @@ -816,7 +848,7 @@ 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"], "kill_chain_phases": ["Delivery"], "nist": ["DE.CM"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = [] providing_technologies = none @@ -841,7 +873,7 @@ 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"], "kill_chain_phases": ["Delivery"], "nist": ["DE.CM"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get Notable Info", "ESCU - Investigate Web Activity From src ip"] +investigative_searches = ["ESCU - Investigate Network Traffic From src ip", "ESCU - Investigate Web Activity From src ip", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = [] providing_technologies = none @@ -859,9 +891,9 @@ id = 2e8948a5-5239-406b-b56b-6c50f1268af3 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"], "nist": ["DE.AE", "DE.DP", "ID.AM"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Notable Info", "ESCU - Investigate AWS activities via region name", "ESCU - Get EC2 Launch Details"] -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 EC2 Launches By User", "ESCU - Previously Seen AWS Regions"] +mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078", "T1535"], "nist": ["DE.AE", "DE.DP", "ID.AM"]} +investigative_searches = ["ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Launch Details", "ESCU - AWS Investigate Security Hub alerts by dest", "ESCU - Get User Information from Identity Table", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable History"] +support_searches = ["ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK", "ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK", "ESCU - Previously Seen EC2 Launches By User", "ESCU - Previously Seen AWS Regions"] data_models = [] 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. @@ -875,9 +907,9 @@ id = 2e8948a5-5239-406b-b56b-6c59f1268af3 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"], "nist": ["DE.AE", "DE.DP"]} +mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078", "T1535"], "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 = 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. @@ -891,9 +923,9 @@ id = 2e8948a5-5239-406b-b56b-6c50w3168af3 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"], "nist": ["DE.CM", "DE.DP", "PR.AC", "PR.DS"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - AWS S3 Bucket details via bucketName", "ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable Info", "ESCU - Investigate AWS activities via region name"] -support_searches = ["ESCU - Previously seen S3 bucket access by remote IP", "ESCU - Baseline of S3 Bucket deletion activity by ARN"] +mappings = {"cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["DE.CM", "DE.DP", "PR.AC", "PR.DS"]} +investigative_searches = ["ESCU - AWS Investigate User Activities By ARN", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get User Information from Identity Table", "ESCU - AWS S3 Bucket details via bucketName", "ESCU - Get Notable Info", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable History"] +support_searches = ["ESCU - Baseline of S3 Bucket deletion activity by ARN", "ESCU - Previously seen S3 bucket access by remote IP"] data_models = [] 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. @@ -910,7 +942,7 @@ 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": ["Command and Control"], "nist": ["PR.AC"]} -investigative_searches = ["ESCU - AWS Network ACL Details from ID", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Risk Modifiers For User", "ESCU - Get All AWS Activity From IP Address", "ESCU - AWS Network ACL Details from ID", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get User Information from Identity Table", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = ["ESCU - Baseline of blocked outbound traffic from AWS"] data_models = [] providing_technologies = none @@ -920,6 +952,23 @@ Amazon's VPC service enables you to launch EC2 instances and leverage other Amaz 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. +[Suspicious Cloud Authentication Activities] +category = Cloud Security +creation_date = 2020-06-04 +modification_date = 2020-06-04 +id = 6380ebbb-55c5-4fce-b754-01fd565fb73c +version = 1 +reference = ["https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/", "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 - DM - Rule"] +mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "nist": ["DE.AE", "DE.DP"]} +investigative_searches = ["ESCU - Investigate AWS User Activities by user field", "ESCU - Get Notable History"] +support_searches = ["ESCU - Update previously seen users in CloudTrail - DM", "ESCU - Previously seen users in CloudTrail - DM"] +data_models = ["Authentication"] +providing_technologies = none +description = Monitor your cloud authentication events. Searches within this Analytic Story leverage the recent cloud updates to the Authentication data model to help you stay aware of and investigate suspicious login activity. +narrative = It is important to monitor and control who has access to your cloud infrastructure. Detecting suspicious logins 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 compute activity whether legitimate or otherwise.\ +This Analytic Story has data model versions of cloud searches leveraging Authentication data, including those looking for suspicious login activity, and cross-account activity for AWS. + [Suspicious Command-Line Executions] category = Adversary Tactics creation_date = 2020-02-03 @@ -929,8 +978,8 @@ 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": ["T1036", "T1059", "T1064"], "nist": ["DE.CM", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] -support_searches = ["ESCU - Baseline of Command Line Length - MLTK", "ESCU - Previously seen command line arguments"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] +support_searches = ["ESCU - Previously seen command line arguments", "ESCU - Baseline of Command Line Length - MLTK"] 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. @@ -945,7 +994,7 @@ 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": ["T1043", "T1048"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.DS", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Parent Process Info"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get User Information from Identity Table", "ESCU - Get DNS Server History for a host", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = ["ESCU - Baseline of DNS Query Length - MLTK"] data_models = ["Network_Resolution"] providing_technologies = none @@ -961,7 +1010,7 @@ 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 - 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 Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get User Information from Identity Table", "ESCU - Get Emails From Specific Sender", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Email Info"] +investigative_searches = ["ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Email Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Emails From Specific Sender", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = ["ESCU - DNSTwist Domain Names"] data_models = ["Email", "UEBA"] providing_technologies = none @@ -981,8 +1030,8 @@ 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": ["T1059", "T1103", "T1131"], "nist": ["DE.AE", "DE.CM", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] -support_searches = ["ESCU - Baseline of Command Line Length - MLTK", "ESCU - Previously seen command line arguments"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] +support_searches = ["ESCU - Previously seen command line arguments", "ESCU - Baseline of Command Line Length - MLTK"] data_models = ["Endpoint"] providing_technologies = none description = Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. @@ -999,7 +1048,7 @@ 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"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} -investigative_searches = ["ESCU - Investigate Okta Activity by app", "ESCU - Investigate Okta Activity by IP Address", "ESCU - Investigate User Activities In Okta"] +investigative_searches = ["ESCU - Investigate User Activities In Okta", "ESCU - Investigate Okta Activity by app", "ESCU - Investigate Okta Activity by IP Address"] support_searches = [] data_models = [] providing_technologies = none @@ -1017,7 +1066,7 @@ 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": ["T1047", "T1084"], "nist": ["PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Parent Process Info"] +investigative_searches = ["ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1035,7 +1084,7 @@ 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": ["T1015", "T1042", "T1103", "T1112", "T1131", "T1138"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1044,6 +1093,23 @@ narrative = Attackers are developing increasingly sophisticated techniques for h 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. +[Suspicious Zoom Child Processes] +category = Adversary Tactics +creation_date = 2020-04-13 +modification_date = 2020-04-13 +id = aa3749a6-49c7-491e-a03f-4eaee5fe0258 +version = 1 +reference = ["https://blog.rapid7.com/2020/04/02/dispelling-zoom-bugbears-what-you-need-to-know-about-the-latest-zoom-vulnerabilities/", "https://threatpost.com/two-zoom-zero-day-flaws-uncovered/154337/"] +detection_searches = ["ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - First Time Seen Child Process of Zoom - Rule"] +mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Exploitation"], "mitre_attack": ["T1059", "T1068"], "nist": ["DE.CM", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get Process Registry Activity", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process File Activity"] +support_searches = ["ESCU - Previously Seen Zoom Child Processes - Update", "ESCU - Previously Seen Zoom Child Processes - Initial"] +data_models = ["Endpoint"] +providing_technologies = none +description = Attackers are using Zoom as an vector to increase privileges on a sytems. This story detects new child processes of zoom and provides investigative actions for this detection. +narrative = Zoom is a leader in modern enterprise video communications and its usage has increased dramatically with a large amount of the population under stay-at-home orders due to the COVID-19 pandemic. With increased usage has come increased scrutiny and several security flaws have been found with this application on both Windows and macOS systems.\ +Current detections focus on finding new child processes of this application on a per host basis. Investigative searches are included to gather information needed during an investigation. + [Unusual AWS EC2 Modifications] category = Cloud Security creation_date = 2018-04-09 @@ -1052,8 +1118,8 @@ id = 73de57ef-0dfc-411f-b1e7-fa24428aeae0 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"], "nist": ["ID.AM"]} -investigative_searches = ["ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable History", "ESCU - AWS Investigate User Activities By ARN"] +mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1078"], "nist": ["ID.AM"]} +investigative_searches = ["ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable History"] support_searches = ["ESCU - Previously Seen EC2 Modifications By User"] data_models = [] providing_technologies = none @@ -1070,7 +1136,7 @@ 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": ["T1015", "T1036", "T1085"], "nist": ["DE.CM", "ID.AM", "PR.DS", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = ["ESCU - Baseline of Command Line Length - MLTK"] data_models = ["Endpoint"] providing_technologies = none @@ -1088,7 +1154,7 @@ version = 1 reference = ["https://www.monkey.org/~dugsong/dsniff/"] detection_searches = ["ESCU - Protocols passing authentication in cleartext - Rule"] mappings = {"cis20": ["CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Network_Traffic"] providing_technologies = none @@ -1104,7 +1170,7 @@ 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": ["T1078", "T1136"], "nist": ["DE.AE", "DE.CM", "DE.DP"]} -investigative_searches = ["ESCU - Get Web Session Information via session id", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Emails From Specific Sender"] +investigative_searches = ["ESCU - Get Notable Info", "ESCU - Get Emails From Specific Sender", "ESCU - Get Web Session Information via session id", "ESCU - Get Notable History"] support_searches = [] data_models = [] providing_technologies = none @@ -1125,7 +1191,7 @@ 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": ["T1089", "T1112"], "nist": ["DE.CM", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1141,7 +1207,7 @@ 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": ["T1042"], "nist": ["DE.CM", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1161,7 +1227,7 @@ 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": ["T1070"], "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 - Investigate Web Activity From Host", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Parent Process Info"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1177,8 +1243,8 @@ id = 30874d4f-20a1-488f-85ec-5d52ef74e3f9 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": ["T1031", "T1050", "T1053", "T1089", "T1103", "T1131", "T1138"], "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 - Investigate Web Activity From Host", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Installation"], "mitre_attack": ["T1050", "T1053", "T1058", "T1103", "T1131", "T1138"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1194,7 +1260,7 @@ 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 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 Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +investigative_searches = ["ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1209,9 +1275,9 @@ id = 6dbd810e-f66d-414b-8dfc-e46de55cbfe2 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 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 Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] -support_searches = ["ESCU - Previously Seen Running Windows Services"] +mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Installation"], "mitre_attack": ["T1050", "T1058"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] +support_searches = ["ESCU - Previously Seen Running Windows Services - Update", "ESCU - Previously Seen Running Windows Services - Initial"] 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. diff --git a/package/default/analyticstories.conf b/package/default/analyticstories.conf index ace2c52f36..5a94fdc3db 100644 --- a/package/default/analyticstories.conf +++ b/package/default/analyticstories.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security-content -# On Date: 2020-04-17T19:06:18 UTC +# On Date: 2020-06-25T22:36:57 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 = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule", "ESCU - Get Notable History", "ESCU - AWS Investigate User Activities By Source User", "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 = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - EC2 Instance Started With Previously Unseen AMI - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get EC2 Launch Details", "ESCU - Get Notable Info", "ESCU - Investigate AWS activities via region name", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - AWS Investigate User Activities By ARN"] 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +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 blocked Outbound Traffic from your AWS - Rule", "ESCU - Detect Spike in Network ACL Activity - Rule", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Notable Info", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host", "ESCU - AWS Network ACL Details from ID", "ESCU - Get All AWS Activity From IP Address"] 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 = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - AWS Cloud Provisioning From Previously Unseen City - 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 IP Address - Rule", "ESCU - Get All AWS Activity From Country", "ESCU - Get All AWS Activity From Region", "ESCU - AWS Investigate Security Hub alerts by dest", "ESCU - Get All AWS Activity From City", "ESCU - Get All AWS Activity From IP Address"] 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Detect Spike in AWS API Activity - Rule", "ESCU - Detect AWS API Activities From Unapproved Accounts - Rule", "ESCU - Detect new API calls from user roles - Rule", "ESCU - Detect API activity from users without MFA - Rule", "ESCU - Detect Spike in Security Group Activity - Rule", "ESCU - Get Notable History", "ESCU - Investigate AWS User Activities by user field", "ESCU - Get Notable Info"] 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 = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Detect Excessive Account Lockouts From Endpoint - Rule", "ESCU - Short Lived Windows Accounts - Rule", "ESCU - Detect Excessive User Account Lockouts - Rule", "ESCU - Identify New User Accounts - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Logon Rights Modifications For User", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Logon Rights Modifications For Endpoint", "ESCU - Get Risk Modifiers For User"] 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 = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +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 User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Suspicious Strings in HTTP Header", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web POSTs From src", "ESCU - Get Risk Modifiers For User"] 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +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"] 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 = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Monitor Web Traffic For Brand Abuse - Rule", "ESCU - Monitor DNS For Brand Abuse - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable Info", "ESCU - Get Email Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Emails From Specific Sender", "ESCU - Get Risk Modifiers For User"] 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 = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule", "ESCU - Abnormally High AWS Instances Launched by User - MLTK - 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 - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Cloud Compute Instance Activities", "ESCU - Get EC2 Launch Details", "ESCU - Get Notable Info", "ESCU - Investigate AWS activities via region name", "ESCU - Investigate User Activities In All Cloud Regions", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Investigate User Activities In Single Cloud Region", "ESCU - AWS Investigate Security Hub alerts by dest", "ESCU - AWS Investigate User Activities By ARN"] 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 = [{"company": "Splunk", "email": "-", "name": "Jose Hernandez"}] +spec_version = 3 +searches = ["ESCU - Processes Tapping Keyboard Events - Rule", "ESCU - Osquery pack - ColdRoot detection - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From src ip", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Vulnerability Logs For Endpoint"] 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 = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Email servers sending high volume traffic to hosts - Rule", "ESCU - Email files written outside of the Outlook directory - 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 Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Info", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User"] 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 = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - 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 - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Notable Info", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host", "ESCU - AWS Network ACL Details from ID", "ESCU - Get All AWS Activity From IP Address"] 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. @@ -185,34 +185,34 @@ 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 = [{"company": "Splunk", "email": "-", "name": "Splunk Research Team"}] +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 -searches = ["ESCU - GCP GCR container uploaded - Rule", "ESCU - New container uploaded to AWS ECR - Rule", "ESCU - Investigate AWS ECR container listing activity"] +maintainers = [{"company": "Rico Valdez, Splunk", "email": "-", "name": "Rod Soto"}] +spec_version = 3 +searches = ["ESCU - New container uploaded to AWS ECR - Rule", "ESCU - GCP GCR container uploaded - 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. [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 = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Creation of Shadow Copy with wmic and powershell - 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 - Creation of Shadow Copy - Rule", "ESCU - Credential Dumping via Symlink to Shadow Copy - Rule", "ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Create Remote Thread into LSASS - Rule", "ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule", "ESCU - Access LSASS Memory for Dump Creation - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Unsigned Image Loaded by LSASS - Rule", "ESCU - Investigate Previous Unseen User", "ESCU - Investigate Failed Logins for Multiple Destinations", "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 = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Detect New Local Admin account - Rule", "ESCU - Create local admin accounts using net exe - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule", "ESCU - Single Letter Process On Endpoint - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Process Registry Activity", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process File Activity", "ESCU - Get Vulnerability Logs For Endpoint"] 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Large Volume of DNS ANY Queries - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info"] 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["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 - Clients Connecting to Multiple DNS Servers - 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Detection of DNS Tunnels - Rule", "ESCU - Detect USB device insertion - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Info", "ESCU - Get Notable Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host"] 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 = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Processes launching netsh - Rule", "ESCU - Attempt To Add Certificate To Untrusted Store - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Unload Sysmon Filter Driver - Rule", "ESCU - Attempt To Stop Security Service - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User"] 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect web traffic to dynamic domain providers - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From src ip", "ESCU - Get Notable Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host"] 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Detection of tools built by NirSoft - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For User", "ESCU - Get Vulnerability Logs For Endpoint"] 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 = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - SMB Traffic Spike - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Create or delete windows shares using net exe - Rule", "ESCU - Suspicious File Write - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host", "ESCU - Get Vulnerability Logs For Endpoint"] 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 = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Windows hosts file modification - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS Server History for a host"] 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +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 - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable Info", "ESCU - Get Vulnerability Logs For Endpoint"] 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 +362,44 @@ 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 = [{"company": "Splunk", "email": "-", "name": "Rod Soto"}] +spec_version = 3 +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 - Kubernetes Azure scan fingerprint - Rule", "ESCU - Kubernetes Azure pod scan fingerprint - Rule", "ESCU - Get Notable History", "ESCU - Amazon EKS Kubernetes activity by src ip", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - GCP Kubernetes activity by src ip"] 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://Kubernetes Sensitive Object Access Activity] +category = Cloud Security +last_updated = 2020-05-20 +version = 1 +references = ["https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rod Soto"}] +spec_version = 3 +searches = ["ESCU - Kubernetes Azure detect service accounts forbidden failure access - Rule", "ESCU - Kubernetes AWS detect service accounts forbidden failure access - Rule", "ESCU - Kubernetes AWS detect suspicious kubectl calls - Rule", "ESCU - Kubernetes Azure detect sensitive object access - Rule", "ESCU - AWS EKS Kubernetes cluster sensitive object access - Rule", "ESCU - Kubernetes Azure detect suspicious kubectl calls - Rule", "ESCU - Get Notable Info"] +description = This story addresses detection and response of accounts acccesing Kubernetes cluster sensitive objects such as configmaps or secrets providing information on items such as user user, group. object, namespace and authorization reason. +narrative = Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive objects within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes sensitive objects. + +[analytic_story://Kubernetes Sensitive Role Activity] +category = Cloud Security +last_updated = 2020-05-20 +version = 1 +references = ["https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rod Soto"}] +spec_version = 3 +searches = ["ESCU - Kubernetes Azure detect sensitive role access - Rule", "ESCU - Kubernetes Azure detect most active service accounts by pod namespace - Rule", "ESCU - Kubernetes AWS detect most active service accounts by pod - Rule", "ESCU - Kubernetes AWS detect RBAC authorization by account - Rule", "ESCU - Kubernetes Azure detect RBAC authorization by account - Rule", "ESCU - Kubernetes AWS detect sensitive role access - Rule", "ESCU - Get Notable Info"] +description = This story addresses detection and response around Sensitive Role usage within a Kubernetes clusters against cluster resources and namespaces. +narrative = Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive roles within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes role activities + [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 = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Detect Activity Related to Pass the Hash Attacks - Rule", "ESCU - Schtasks scheduling job on remote system - Rule", "ESCU - Kerberoasting spn request with RC4 encryption - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User"] 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 +410,11 @@ 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 = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Malicious PowerShell Process - Encoded Command - Rule", "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 - Multiple Suspicious Command-Line Arguments - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Info", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User"] 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 +430,22 @@ 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 = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Unsuccessful Netbackup backups - Rule", "ESCU - Extended Period Without Successful Netbackup Backups - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - All backup logs for host", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] 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 = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Vulnerability Logs For Endpoint"] 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 +453,11 @@ 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 = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - No Windows Updates in a time frame - Rule", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info"] 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 +466,11 @@ 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 -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"] +version = 1 +references = ["https://docs.microsoft.com/en-us/previous-versions/tn-archive/bb490939(v=technet.10)", "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": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Processes launching netsh - Rule", "ESCU - Processes created by netsh - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User"] 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 +478,11 @@ 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 = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - First Time Seen Running Windows Service - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User"] 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 +493,11 @@ 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 = [{"company": "Splunk", "email": "-", "name": "Splunk Research Team"}] +spec_version = 3 +searches = ["ESCU - Suspicious LNK file launching a process - Rule", "ESCU - Detect Oulook exe writing a zip file - 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 +511,11 @@ 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 = [{"company": "iDefense", "email": "-", "name": "iDefense Cyber Espionage Team"}] +spec_version = 3 +searches = ["ESCU - Unusually Long Command Line - Rule", "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 - First time seen command line argument - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For User"] 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 +549,33 @@ 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 = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS Server History for a host"] 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 = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - Scheduled tasks used in BadRabbit ransomware - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable Info", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For User", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint"] 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Detect New Login Attempts to Routers - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Risk Modifiers For User"] 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 +583,11 @@ 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - SQL Injection with Long URLs - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info"] 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 +595,11 @@ 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 = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Remote Desktop Network Bruteforce - Rule", "ESCU - Samsam Test File Write - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - File with Samsam Extension - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Batch File Write to System32 - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint"] 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 +611,22 @@ 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 = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Spectre and Meltdown Vulnerable Systems - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Risk Modifiers For User"] 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Open Redirect in Splunk Web - Rule", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info"] 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 +642,11 @@ 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 = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Splunk Enterprise Information Disclosure - Rule", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From src ip", "ESCU - Investigate Network Traffic 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 +656,33 @@ 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["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 - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get EC2 Launch Details", "ESCU - Get Notable Info", "ESCU - Investigate AWS activities via region name", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - AWS Investigate Security Hub alerts by dest", "ESCU - AWS Investigate User Activities By ARN"] 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +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 AWS Console Login by User from New Country - Rule", "ESCU - Detect new user AWS Console Login - 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +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 - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Investigate AWS activities via region name", "ESCU - AWS S3 Bucket details via bucketName", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get All AWS Activity From IP Address"] 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.\ @@ -669,47 +691,59 @@ 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Notable Info", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host", "ESCU - AWS Network ACL Details from ID", "ESCU - Get All AWS Activity From IP Address"] 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. +[analytic_story://Suspicious Cloud Authentication Activities] +category = Cloud Security +last_updated = 2020-06-04 +version = 1 +references = ["https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/", "https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +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 AWS Console Login by User from New Country - Rule", "ESCU - Detect new user AWS Console Login - DM - Rule", "ESCU - Get Notable History", "ESCU - Investigate AWS User Activities by user field"] +description = Monitor your cloud authentication events. Searches within this Analytic Story leverage the recent cloud updates to the Authentication data model to help you stay aware of and investigate suspicious login activity. +narrative = It is important to monitor and control who has access to your cloud infrastructure. Detecting suspicious logins 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 compute activity whether legitimate or otherwise.\ +This Analytic Story has data model versions of cloud searches leveraging Authentication data, including those looking for suspicious login activity, and cross-account activity for AWS. + [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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Unusually Long Command Line - Rule", "ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For User"] 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 = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Excessive DNS Failures - 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 - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Info", "ESCU - Get Notable Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host"] 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Suspicious Email - UBA Anomaly - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable Info", "ESCU - Get Email Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Emails From Specific Sender", "ESCU - Get Risk Modifiers For User"] 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 +754,11 @@ 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Detect mshta exe running scripts in command-line arguments - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For User"] 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 +767,11 @@ 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 = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Okta Account Lockout Events - Rule", "ESCU - Multiple Okta Users With Invalid Credentails From The Same IP - Rule", "ESCU - Okta Failed SSO Attempts - Rule", "ESCU - Okta User Logins From Multiple Cities - Rule", "ESCU - Investigate Okta Activity by app", "ESCU - Investigate User Activities In Okta", "ESCU - Investigate Okta Activity by IP Address"] 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 +780,11 @@ 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 = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - WMI Temporary Event Subscription - Rule", "ESCU - Remote WMI Command Attempt - Rule", "ESCU - WMI Permanent Event Subscription - Sysmon - Rule", "ESCU - WMI Permanent Event Subscription - Rule", "ESCU - Process Execution via WMI - Rule", "ESCU - Script Execution via WMI - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Info", "ESCU - Get Notable Info", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User"] 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.\ @@ -759,24 +793,36 @@ 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Disabling Remote User Account Control - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For User"] 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.\ The searches in this story are designed to help you detect behaviors associated with manipulation of the Windows registry. +[analytic_story://Suspicious Zoom Child Processes] +category = Adversary Tactics +last_updated = 2020-04-13 +version = 1 +references = ["https://blog.rapid7.com/2020/04/02/dispelling-zoom-bugbears-what-you-need-to-know-about-the-latest-zoom-vulnerabilities/", "https://threatpost.com/two-zoom-zero-day-flaws-uncovered/154337/"] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - First Time Seen Child Process of Zoom - Rule", "ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Get Process File Activity", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Registry Activity"] +description = Attackers are using Zoom as an vector to increase privileges on a sytems. This story detects new child processes of zoom and provides investigative actions for this detection. +narrative = Zoom is a leader in modern enterprise video communications and its usage has increased dramatically with a large amount of the population under stay-at-home orders due to the COVID-19 pandemic. With increased usage has come increased scrutiny and several security flaws have been found with this application on both Windows and macOS systems.\ +Current detections focus on finding new child processes of this application on a per host basis. Investigative searches are included to gather information needed during an investigation. + [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 = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - EC2 Instance Modified With Previously Unseen User - Rule", "ESCU - Get Notable History", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - AWS Investigate User Activities By ARN"] 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 +830,11 @@ 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Unusually Long Command Line - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - RunDLL Loading DLL By Ordinal - Rule", "ESCU - Detect processes used for System Network Configuration Discovery - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User"] 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 +843,22 @@ 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Protocols passing authentication in cleartext - Rule", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For User"] 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 = [{"company": "Splunk", "email": "-", "name": "Jim Apger"}] +spec_version = 3 +searches = ["ESCU - Web Fraud - Account Harvesting - Rule", "ESCU - Web Fraud - Password Sharing Across Accounts - Rule", "ESCU - Web Fraud - Anomalous User Clickspeed - Rule", "ESCU - Get Notable History", "ESCU - Get Web Session Information via session id", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable Info"] 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 +870,22 @@ 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 = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Disabling Remote User Account Control - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Info", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For User"] 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 = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +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 Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Info", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For User"] 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 +896,11 @@ 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 = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Deleting Shadow Copies - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Vulnerability Logs For Endpoint"] 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 +908,33 @@ 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 = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Detect Path Interception By Creation Of program exe - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Shim Database File Creation - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Shim Database Installation With Suspicious Parameters - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For User"] 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 = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Overwriting Accessibility Binaries - Rule", "ESCU - Child Processes of Spoolsv exe - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For User"] 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 = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Info", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User"] 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 +946,367 @@ 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"]} +annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1535"], "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 = [] [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"]} +annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1535"], "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 = [] [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 = [] [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"]} +annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1535"], "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 = [] [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"], "mitre_attack": ["T1078"], "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 = [] + +[savedsearch://ESCU - AWS EKS Kubernetes cluster sensitive object access - Rule] +type = detection +asset_type = AWS EKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets +how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. +providing_technologies = [] [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 = [] [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 = [] [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"], "mitre_attack": ["T1078"], "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 = [] [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"], "mitre_attack": ["T1078"], "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 = [] [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"], "mitre_attack": ["T1078"], "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 = [] [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"], "mitre_attack": ["T1078"], "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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] -[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 = [] [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 = [] -[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 = [] [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 = [] [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"]} +annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078"], "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 = [] [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 = [] [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 = [] [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"], "mitre_attack": ["T1535"], "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 = [] [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 = [] [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 = [] [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 = [] -[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 = [] -[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 = [] [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 = [] [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. -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"]} +explanation = This search detects the use of wmic and Powershell to create a shadow copy. +how_to_implement = none +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 = [] [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 = [] [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 = [] [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 +1314,59 @@ 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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 +1374,15 @@ 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 = [] [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 +1390,143 @@ 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"], "mitre_attack": ["T1078"], "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 = [] [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"], "mitre_attack": ["T1535"], "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 = [] [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"], "mitre_attack": ["T1535"], "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 = [] [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"], "mitre_attack": ["T1535"], "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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 +1534,105 @@ 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 = [] [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 = [] [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"], "mitre_attack": ["T1530"], "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 = [] -[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 = [] [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 = [] -[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 = [] -[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 = [] [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 = [] [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 = [] [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"], "mitre_attack": ["T1530"], "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 = [] [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 +1640,95 @@ 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"], "mitre_attack": ["T1078"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} known_false_positives = -providing_technologies = ["AWS"] +providing_technologies = [] [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 = [] [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"], "mitre_attack": ["T1530"], "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 = [] [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"], "mitre_attack": ["T1078"], "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 = [] [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 = [] [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 = [] [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 = [] -[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 = [] [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 = [] [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 +1736,1372 @@ 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 = [] [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 = [] -[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 = [] [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"]} +annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078", "T1098"], "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 = [] [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"], "mitre_attack": ["T1078"], "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 = [] + +[savedsearch://ESCU - Detect new user AWS Console Login - DM - Rule] +type = detection +asset_type = AWS Instance +confidence = medium +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 and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. 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"], "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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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"]} +annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078"], "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 = [] [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"], "mitre_attack": ["T1078", "T1535"], "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 = [] [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 = [] [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 = [] [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"]} +annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078"], "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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] + +[savedsearch://ESCU - First Time Seen Child Process of Zoom - Rule] +type = detection +asset_type = Endpoint +confidence = medium +explanation = This search looks for child processes spawned by zoom.exe or zoom.us 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 should run the baseline search `Previously Seen Zoom Child Processes - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Zoom Child Processes - Update` to keep this table up to date and to age out old child processes. Please update the `previously_seen_zoom_child_processes_window` macro to adjust the time window. +annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1068"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +known_false_positives = A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken. +providing_technologies = [] [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 = [] [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 = [] [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 = {"mitre_attack": ["T1525"]} 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 = [] [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 = [] -[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 = [] [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 = [] [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 = [] + +[savedsearch://ESCU - Kerberoasting spn request with RC4 encryption - Rule] +type = detection +asset_type = Endpoint +confidence = medium +explanation = This search detects a potential kerberoasting attack via service principal name requests +how_to_implement = You must be ingesting endpoint data that tracks process activity, and include the windows security event logs that contain kerberos +annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1208"], "nist": ["DE.CM"]} +known_false_positives = Older systems that support kerberos RC4 by default NetApp may generate false positives +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes AWS detect RBAC authorization by account - Rule] +type = detection +asset_type = AWS EKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences +how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes AWS detect most active service accounts by pod - Rule] +type = detection +asset_type = AWS EKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision +how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes AWS detect sensitive role access - Rule] +type = detection +asset_type = AWS EKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes AWS detect service accounts forbidden failure access - Rule] +type = detection +asset_type = AWS EKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI +how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = This search can give false positives as there might be inherent issues with authentications and permissions at cluster. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes AWS detect suspicious kubectl calls - Rule] +type = detection +asset_type = AWS EKS Kubernetes cluster +confidence = medium +explanation = This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context +how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect RBAC authorization by account - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect most active service accounts by pod namespace - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Not all service accounts interactions are malicious. Analyst must consider IP and verb context when trying to detect maliciousness. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect sensitive object access - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect sensitive role access - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect service accounts forbidden failure access - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes service accounts with failure or forbidden access status +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = This search can give false positives as there might be inherent issues with authentications and permissions at cluster. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect suspicious kubectl calls - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on rare Kubectl calls with IP, verb namespace and object access context +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially suspicious IPs and sensitive objects such as configmaps or secrets +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure pod scan fingerprint - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Reconnaissance"]} +known_false_positives = Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure scan fingerprint - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Reconnaissance"]} +known_false_positives = Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. +providing_technologies = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = {"mitre_attack": ["T1525"]} known_false_positives = Uploading container is a normal behavior from developers or users with access to container registry. -providing_technologies = ["AWS"] +providing_technologies = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] -[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. -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"]} +confidence = medium +explanation = The search looks for reg.exe modifying registry keys that define Windows services and their configurations. +how_to_implement = none +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1058"], "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 = [] -[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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] -[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. -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"]} +explanation = This search looks for arguments to sc.exe indicating the creation or modification of a Windows service. +how_to_implement = none +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1050"], "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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] -[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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] [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 = [] ### END DETECTIONS ### -### INVESTIGATIONS ### +### RESPONSE TASKS ### + +[savedsearch://ESCU - AWS Investigate Security Hub alerts by dest] +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 = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [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 +3109,49 @@ 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 +3159,39 @@ 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 +3199,7 @@ 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 +3207,7 @@ 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 +3215,7 @@ 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 +3223,247 @@ 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 +3471,7 @@ 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 +3479,7 @@ 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 +3487,482 @@ 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 Zoom Child Processes - Initial] +type = support +explanation = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS). This table is outputed to disk. +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. +known_false_positives = not defined +providing_technologies = none + +[savedsearch://ESCU - Previously Seen Zoom Child Processes - Update] +type = support +explanation = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS) within the last hour. It then updates this information with historical data and filters out proces_name and endpoint pairs that have not been seen within the specified time window. This updated table is outputed to disk. +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. +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 - Previously seen users in CloudTrail - DM] +type = support +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 username, within the last 30 days. +how_to_implement = You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Validate the user name entries in `previously_seen_users_console_logins.csv`, which is a lookup file created by this support search. +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 - Update previously seen users in CloudTrail - DM] +type = support +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 user, within the last hour. +how_to_implement = You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Validate the user name entries in `previously_seen_users_console_logins.csv`, which is a lookup file created by this support search. +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/app.conf b/package/default/app.conf index ec718c8dfd..da8305adad 100644 --- a/package/default/app.conf +++ b/package/default/app.conf @@ -4,7 +4,7 @@ is_configured = false state = enabled state_change_requires_restart = false -build = 5208 +build = 6430 [triggers] reload.analytic_stories = simple @@ -12,7 +12,6 @@ reload.usage_searches = simple reload.use_case_library = simple reload.correlationsearches = simple reload.analyticstories = simple -reload.ui-tour = simple reload.governance = simple reload.managed_configurations = simple reload.postprocess = simple @@ -20,7 +19,7 @@ reload.content-version = simple [launcher] author = Splunk -version = 1.0.54 +version = 3.0.3 description = Explore the Analytic Stories included with ES Content Updates. [ui] diff --git a/package/default/collections.conf b/package/default/collections.conf index f36b4c99f9..ed0c87d151 100644 --- a/package/default/collections.conf +++ b/package/default/collections.conf @@ -1,13 +1,15 @@ -[kvstore_process_length] -field.process_name = string -field.dest = string -field.length = number -field.stddev = number -accelerated_fields.my_acceleration = {"process_name": 1, "dest": 1} +############# +# Automatically generated by generator.py in splunk/security-content +# On Date: 2020-06-26T13:32:24 UTC +# Author: Splunk Security Research +# Contact: research@splunk.com +############# -[kvstore_process_path] -field.process_name = string -field.dest = string -field.process_path = string -accelerated_fields.my_acceleration = {"process_name": 1, "dest": 1} +[previously_seen_running_windows_services] +enforceTypes = false +replicate = false + +[zoom_first_time_child_process] +enforceTypes = false +replicate = false diff --git a/package/default/content-version.conf b/package/default/content-version.conf index 4a2967788c..130dcdfca6 100644 --- a/package/default/content-version.conf +++ b/package/default/content-version.conf @@ -1,2 +1,2 @@ [content-version] -version = 1.0.54 +version = 3.0.3 diff --git a/package/default/data/ui/panels/workbench_panel_aws_investigate_security_hub_alerts_by_dest.xml b/package/default/data/ui/panels/workbench_panel_aws_investigate_security_hub_alerts_by_dest.xml new file mode 100644 index 0000000000..2ed2eb4181 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_aws_investigate_security_hub_alerts_by_dest.xml @@ -0,0 +1,9 @@ + + + + sourcetype="aws:securityhub:firehose" "findings{}.Resources{}.Type"=AWSEC2Instance | rex field=findings{}.Resources{}.Id .*instance/(?<instance>.*) | search instance = $dest$ |rename findings{}.* as * | rename Remediation.Recommendation.Text as Remediation | table instance Title ProductArn Description FirstObservedAt RecordState Remediation + + + +
+ \ 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 index b4642ac7a7..73fd273db3 100644 --- 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 @@ -1,7 +1,7 @@ - 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 + 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 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 index 30e3630ed5..fabf2baa48 100644 --- 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 @@ -1,7 +1,7 @@
- |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 + |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 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 index eaacf46c7b..56dcaccf83 100644 --- 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 @@ -1,7 +1,7 @@
- | 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")` + | 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")` 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 index 02d2dff20d..90efb8b630 100644 --- 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 @@ -1,7 +1,7 @@
- `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 + `wineventlog_security` EventCode=4768 OR EventCode=4769 | rex field=user "(?<new_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 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 index ab5940c8fe..e5eaca5a72 100644 --- 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 @@ -1,7 +1,7 @@
- | 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")` + | 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")` 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 index bb306f81a8..01f3ae513f 100644 --- 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 @@ -1,7 +1,7 @@
- | 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 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" (?<suspicious_strings>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 diff --git a/package/default/es_investigations.conf b/package/default/es_investigations.conf index 385a5bdadd..eb06aaa137 100644 --- a/package/default/es_investigations.conf +++ b/package/default/es_investigations.conf @@ -3,79 +3,79 @@ 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_get_notable_history", "panel://workbench_panel_aws_investigate_user_activities_by_accesskeyid", "panel://workbench_panel_aws_investigate_user_activities_by_source_user"] +panels = ["panel://workbench_panel_aws_investigate_user_activities_by_accesskeyid", "panel://workbench_panel_aws_investigate_user_activities_by_source_user", "panel://workbench_panel_get_notable_history"] [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_notable_history", "panel://workbench_panel_get_ec2_instance_details_by_instanceid", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_investigate_aws_activities_via_region_name", "panel://workbench_panel_get_ec2_launch_details"] +panels = ["panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_ec2_launch_details", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_ec2_instance_details_by_instanceid", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_investigate_aws_activities_via_region_name", "panel://workbench_panel_get_notable_history"] [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_aws_network_interface_details_via_resourceid", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_dns_traffic_ratio"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_aws_network_acl_details_from_id", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_aws_network_interface_details_via_resourceid", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_city", "panel://workbench_panel_get_all_aws_activity_from_region", "panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_get_all_aws_activity_from_country"] +panels = ["panel://workbench_panel_get_all_aws_activity_from_city", "panel://workbench_panel_get_all_aws_activity_from_country", "panel://workbench_panel_aws_investigate_security_hub_alerts_by_dest", "panel://workbench_panel_get_all_aws_activity_from_region", "panel://workbench_panel_get_all_aws_activity_from_ip_address"] [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_investigate_aws_user_activities_by_user_field", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_notable_info"] +panels = ["panel://workbench_panel_investigate_aws_user_activities_by_user_field", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_logon_rights_modifications_for_user"] +panels = ["panel://workbench_panel_get_logon_rights_modifications_for_user", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_logon_rights_modifications_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_posts_from_src", "panel://workbench_panel_investigate_suspicious_strings_in_http_header", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_suspicious_strings_in_http_header", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_investigate_web_posts_from_src", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_first_occurrence_and_last_occurrence_of_a_mac_address", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_notable_info"] +panels = ["panel://workbench_panel_get_first_occurrence_and_last_occurrence_of_a_mac_address", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_emails_from_specific_sender", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_email_info"] +panels = ["panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_email_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_emails_from_specific_sender", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_notable_history", "panel://workbench_panel_get_ec2_instance_details_by_instanceid", "panel://workbench_panel_investigate_user_activities_in_all_cloud_regions", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_investigate_cloud_compute_instance_activities", "panel://workbench_panel_investigate_user_activities_in_single_cloud_region", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_investigate_aws_activities_via_region_name", "panel://workbench_panel_get_ec2_launch_details"] +panels = ["panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_ec2_launch_details", "panel://workbench_panel_investigate_user_activities_in_single_cloud_region", "panel://workbench_panel_aws_investigate_security_hub_alerts_by_dest", "panel://workbench_panel_investigate_user_activities_in_all_cloud_regions", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_investigate_cloud_compute_instance_activities", "panel://workbench_panel_get_ec2_instance_details_by_instanceid", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_investigate_aws_activities_via_region_name", "panel://workbench_panel_get_notable_history"] [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_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_network_traffic_from_src_ip", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_investigate_web_activity_from_src_ip"] +panels = ["panel://workbench_panel_investigate_network_traffic_from_src_ip", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_investigate_web_activity_from_src_ip", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_aws_network_interface_details_via_resourceid", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_aws_network_acl_details_from_id", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_aws_network_interface_details_via_resourceid", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [panel_group://workbench_panel_group_common_phishing_frameworks] label = Common Phishing Frameworks @@ -93,19 +93,19 @@ panels = ["panel://workbench_panel_investigate_aws_ecr_container_listing_activit 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_pass_the_hash_attempts", "panel://workbench_panel_investigate_failed_logins_for_multiple_destinations", "panel://workbench_panel_investigate_previous_unseen_user", "panel://workbench_panel_investigate_pass_the_ticket_attempts"] +panels = ["panel://workbench_panel_investigate_previous_unseen_user", "panel://workbench_panel_investigate_pass_the_hash_attempts", "panel://workbench_panel_investigate_failed_logins_for_multiple_destinations", "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_process_registry_activity", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_process_file_activity"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_registry_activity", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_process_file_activity", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_history", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [panel_group://workbench_panel_group_dns_hijacking] label = DNS Hijacking @@ -117,91 +117,103 @@ panels = ["panel://workbench_panel_get_dns_server_history_for_a_host"] 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_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_dns_traffic_ratio"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_investigate_web_activity_from_src_ip"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_investigate_web_activity_from_src_ip", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_update_logs_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_update_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_investigate_successful_remote_desktop_authentications", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_successful_remote_desktop_authentications", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_dns_server_history_for_a_host"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_investigate_web_activity_from_host", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_vulnerability_logs_for_endpoint"] +panels = ["panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_amazon_eks_kubernetes_activity_by_src_ip", "panel://workbench_panel_get_notable_info"] +panels = ["panel://workbench_panel_amazon_eks_kubernetes_activity_by_src_ip", "panel://workbench_panel_gcp_kubernetes_activity_by_src_ip", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] + +[panel_group://workbench_panel_group_kubernetes_sensitive_object_access_activity] +label = Kubernetes Sensitive Object Access Activity +description = This story addresses detection and response of accounts acccesing Kubernetes cluster sensitive objects such as configmaps or secrets providing information on items such as user user, group. object, namespace and authorization reason. +disabled = 0 +panels = ["panel://workbench_panel_get_notable_info"] + +[panel_group://workbench_panel_group_kubernetes_sensitive_role_activity] +label = Kubernetes Sensitive Role Activity +description = This story addresses detection and response around Sensitive Role usage within a Kubernetes clusters against cluster resources and namespaces. +disabled = 0 +panels = ["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_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_investigate_successful_remote_desktop_authentications", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_successful_remote_desktop_authentications", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_all_backup_logs_for_host", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_endpoint"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_all_backup_logs_for_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_update_logs_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_update_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_history", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint"] +panels = ["panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [panel_group://workbench_panel_group_phishing_payloads] label = Phishing Payloads @@ -213,61 +225,61 @@ panels = ["panel://workbench_panel_get_parent_process_info"] 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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_update_logs_for_endpoint", "panel://workbench_panel_get_backup_logs_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_sysmon_wmi_activity_for_host", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_sysmon_wmi_activity_for_host", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_update_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_backup_logs_for_endpoint", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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"] +panels = ["panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_investigate_successful_remote_desktop_authentications", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_update_logs_for_endpoint", "panel://workbench_panel_get_backup_logs_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_user", "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_user_information_from_identity_table", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_backup_logs_for_endpoint", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_history", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint"] +panels = ["panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_investigate_network_traffic_from_src_ip", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_investigate_web_activity_from_src_ip"] +panels = ["panel://workbench_panel_investigate_network_traffic_from_src_ip", "panel://workbench_panel_investigate_web_activity_from_src_ip", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_notable_history", "panel://workbench_panel_get_ec2_instance_details_by_instanceid", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_investigate_aws_activities_via_region_name", "panel://workbench_panel_get_ec2_launch_details"] +panels = ["panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_ec2_launch_details", "panel://workbench_panel_aws_investigate_security_hub_alerts_by_dest", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_ec2_instance_details_by_instanceid", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_investigate_aws_activities_via_region_name", "panel://workbench_panel_get_notable_history"] [panel_group://workbench_panel_group_suspicious_aws_login_activities] label = Suspicious AWS Login Activities @@ -279,118 +291,146 @@ panels = ["panel://workbench_panel_aws_investigate_user_activities_by_arn"] 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_notable_history", "panel://workbench_panel_aws_s3_bucket_details_via_bucketname", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_investigate_aws_activities_via_region_name"] +panels = ["panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_aws_s3_bucket_details_via_bucketname", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_investigate_aws_activities_via_region_name", "panel://workbench_panel_get_notable_history"] [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_aws_network_interface_details_via_resourceid", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_dns_traffic_ratio"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_aws_network_acl_details_from_id", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_aws_network_interface_details_via_resourceid", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] + +[panel_group://workbench_panel_group_suspicious_cloud_authentication_activities] +label = Suspicious Cloud Authentication Activities +description = Monitor your cloud authentication events. Searches within this Analytic Story leverage the recent cloud updates to the Authentication data model to help you stay aware of and investigate suspicious login activity. +disabled = 0 +panels = ["panel://workbench_panel_investigate_aws_user_activities_by_user_field", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_emails_from_specific_sender", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_email_info"] +panels = ["panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_email_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_emails_from_specific_sender", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_okta_activity_by_app", "panel://workbench_panel_investigate_okta_activity_by_ip_address", "panel://workbench_panel_investigate_user_activities_in_okta"] +panels = ["panel://workbench_panel_investigate_user_activities_in_okta", "panel://workbench_panel_investigate_okta_activity_by_app", "panel://workbench_panel_investigate_okta_activity_by_ip_address"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_sysmon_wmi_activity_for_host", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_sysmon_wmi_activity_for_host", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] + +[panel_group://workbench_panel_group_suspicious_zoom_child_processes] +label = Suspicious Zoom Child Processes +description = Attackers are using Zoom as an vector to increase privileges on a sytems. This story detects new child processes of zoom and provides investigative actions for this detection. +disabled = 0 +panels = ["panel://workbench_panel_get_process_registry_activity", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_process_file_activity"] [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_get_ec2_instance_details_by_instanceid", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_aws_investigate_user_activities_by_arn"] +panels = ["panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_ec2_instance_details_by_instanceid", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_process_information_for_port_activity", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_web_session_information_via_session_id", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_emails_from_specific_sender"] +panels = ["panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_emails_from_specific_sender", "panel://workbench_panel_get_web_session_information_via_session_id", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] [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_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info"] +panels = ["panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_history"] +[panel://workbench_panel_aws_investigate_security_hub_alerts_by_dest] +label = AWS Investigate Security Hub alerts by dest +description = This search retrieves the all the alerts created by AWS Security Hub for a specific dest(instance_id). +disabled = 0 +tokens = {\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + [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. diff --git a/package/default/macros.conf b/package/default/macros.conf index ec0c5df81e..6da9f80e88 100644 --- a/package/default/macros.conf +++ b/package/default/macros.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security-content -# On Date: 2020-05-14T08:00:44 UTC +# On Date: 2020-06-26T13:32:24 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# @@ -10,6 +10,10 @@ definition = sourcetype="aws:cloudwatchlogs:eks" description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. +[aws_cross_account_activity_from_previously_unseen_account___dm_filter] +definition = search * +description = Use this macro to add additional filters + [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. @@ -50,6 +54,10 @@ description = customer specific splunk configurations(eg- index, source, sourcet definition = search * description = Use this macro to add additional filter for create or delete windows shares +[detect_new_user_aws_console_login___dm_filter] +definition = search * +description = Use this macro to add additional filters + [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. @@ -106,6 +114,42 @@ description = customer specific splunk configurations(eg- index, source, sourcet 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_azure] +definition = sourcetype=mscs:storage:blob:json +description = customer specific splunk configurations(eg- index, source, sourcetype) for Kubernetes data from Azure. Replace the macro definition with configurations for your Splunk Environmnent. + +[kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace_filter] +definition = search * +description = Use this macro to add additional filters + +[kubernetes_azure_detect_rbac_authorization_by_account_filter] +definition = search * +description = Use this macro to add additional filters + +[kubernetes_azure_detect_sensitive_object_access_filter] +definition = search * +description = Use this macro to add additional filters + +[kubernetes_azure_detect_sensitive_role_access_filter] +definition = search * +description = Use this macro to add additional filters + +[kubernetes_azure_detect_service_accounts_forbidden_failure_access_filter] +definition = search * +description = Use this macro to add additional filters + +[kubernetes_azure_detect_suspicious_kubectl_calls_filter] +definition = search * +description = Use this macro to add additional filters + +[kubernetes_azure_pod_scan_fingerprint_detection_filter] +definition = search * +description = Use this macro to add additional filters + +[kubernetes_azure_scan_fingerprint_filter] +definition = search * +description = Use this macro to add additional filters + [netbackup] definition = sourcetype="netbackup_logs" description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. @@ -134,6 +178,22 @@ description = Use this macro to determine how far into the past the window shoul 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 +[previously_seen_windows_service_window] +definition = -70m@m +description = Use this macro to determine how far back you should be checking for new Windows services + +[previously_seen_windows_services_forget_window] +definition = -90d@d +description = Use this macro to determine how long to keep track of Windows services + +[previously_seen_zoom_child_processes_forget_window] +definition = -90d@d +description = Use this macro to determine how long to keep track of zoom child processes + +[previously_seen_zoom_child_processes_window] +definition = -70m@m +description = Use this macro to determine how far back you should be checking for new zoom child processes + [prohibited_apps_launching_cmd] definition = | inputlookup prohibited_apps_launching_cmd | rename prohibited_applications as parent_process_name | eval parent_process_name="*" . parent_process_name | table parent_process_name description = This macro outputs a list of process that should not be the parent process of cmd.exe @@ -164,7 +224,7 @@ definition = convert timeformat="%m/%d/%Y %H:%M:%S" ctime($field$) description = convert epoch time to string [security_content_summariesonly] -definition = summariesonly=true allow_old_summaries=true +definition = summariesonly=false allow_old_summaries=true description = search data model's summaries only [security_group_api_calls] @@ -227,6 +287,10 @@ description = Update this macro to limit the output results to filter out false definition = search * description = Update this macro to limit the output results to filter out false positives. +[aws_eks_kubernetes_cluster_sensitive_object_access_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. @@ -515,6 +579,10 @@ description = Update this macro to limit the output results to filter out false definition = search * description = Update this macro to limit the output results to filter out false positives. +[detect_new_user_aws_console_login___dm_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. @@ -591,6 +659,10 @@ description = Update this macro to limit the output results to filter out false definition = search * description = Update this macro to limit the output results to filter out false positives. +[first_time_seen_child_process_of_zoom_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. @@ -619,6 +691,62 @@ description = Update this macro to limit the output results to filter out false definition = search * description = Update this macro to limit the output results to filter out false positives. +[kerberoasting_spn_request_with_rc4_encryption_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_aws_detect_rbac_authorization_by_account_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_aws_detect_most_active_service_accounts_by_pod_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_aws_detect_sensitive_role_access_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_aws_detect_service_accounts_forbidden_failure_access_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_aws_detect_suspicious_kubectl_calls_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_azure_detect_rbac_authorization_by_account_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_azure_detect_sensitive_object_access_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_azure_detect_sensitive_role_access_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_azure_detect_service_accounts_forbidden_failure_access_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_azure_detect_suspicious_kubectl_calls_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_azure_pod_scan_fingerprint_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_azure_scan_fingerprint_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. diff --git a/package/default/savedsearches.conf b/package/default/savedsearches.conf index 84401dfc9c..e3fea60075 100644 --- a/package/default/savedsearches.conf +++ b/package/default/savedsearches.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security-content -# On Date: 2020-05-14T08:00:44 UTC +# On Date: 2020-06-26T13:32:24 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# @@ -11,7 +11,7 @@ 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.mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1535"], "nist": ["ID.AM"]} 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. @@ -24,9 +24,9 @@ action.escu.full_search_name = ESCU - AWS Cloud Provisioning From Previously Uns action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule schedule_window = auto @@ -52,7 +52,7 @@ search = `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceI 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.mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1535"], "nist": ["ID.AM"]} 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. @@ -65,9 +65,9 @@ action.escu.full_search_name = ESCU - AWS Cloud Provisioning From Previously Uns action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule schedule_window = auto @@ -106,9 +106,9 @@ action.escu.full_search_name = ESCU - AWS Cloud Provisioning From Previously Uns action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule schedule_window = auto @@ -134,7 +134,7 @@ search = `cloudtrail` (eventName=Run* OR eventName=Create*) [search `cloudtrail` 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.mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1535"], "nist": ["ID.AM"]} 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. @@ -147,9 +147,9 @@ action.escu.full_search_name = ESCU - AWS Cloud Provisioning From Previously Uns action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule schedule_window = auto @@ -175,7 +175,7 @@ search = `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceI 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"], "nist": ["PR.AC", "PR.DS", "DE.AE"]} +action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "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. @@ -187,9 +187,9 @@ action.escu.full_search_name = ESCU - AWS Cross Account Activity From Previously action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Cross Account Activity"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule schedule_window = auto @@ -209,7 +209,46 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -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` +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` + +[ESCU - AWS EKS Kubernetes cluster sensitive object access - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets +action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets +action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. +action.escu.known_false_positives = Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. +action.escu.creation_date = 2020-06-23 +action.escu.modification_date = 2020-06-23 +action.escu.confidence = high +action.escu.full_search_name = ESCU - AWS EKS Kubernetes cluster sensitive object access - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Sensitive Object Access Activity"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - AWS EKS Kubernetes cluster sensitive object access - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets +action.notable.param.rule_title = AWS EKS Kubernetes cluster sensitive object access +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +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 +is_visible = false +search = `aws_cloudwatchlogs_eks` objectRef.resource=secrets OR configmaps sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 |table sourceIPs{} user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason |dedup user.username user.groups{} |`aws_eks_kubernetes_cluster_sensitive_object_access_filter` [ESCU - AWS Network Access Control List Created with All Open Ports - Rule] action.escu = 0 @@ -227,9 +266,9 @@ action.escu.full_search_name = ESCU - AWS Network Access Control List Created wi action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Network ACL Activity"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - AWS Network Access Control List Created with All Open Ports - Rule schedule_window = auto @@ -267,9 +306,9 @@ action.escu.full_search_name = ESCU - AWS Network Access Control List Deleted - action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Network ACL Activity"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - AWS Network Access Control List Deleted - Rule schedule_window = auto @@ -295,7 +334,7 @@ search = `cloudtrail` eventName=DeleteNetworkAcl|rename userIdentity.arn as arn 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"], "nist": ["DE.DP", "DE.AE"]} +action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "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. @@ -307,9 +346,9 @@ action.escu.full_search_name = ESCU - Abnormally High AWS Instances Launched by action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Abnormally High AWS Instances Launched by User - Rule schedule_window = auto @@ -334,7 +373,7 @@ search = `cloudtrail` eventName=RunInstances errorCode=success | bucket span=10m 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"], "nist": ["DE.DP", "DE.AE"]} +action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "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. @@ -346,9 +385,9 @@ action.escu.full_search_name = ESCU - Abnormally High AWS Instances Launched by action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Cloud Cryptomining", "Suspicious AWS EC2 Activities"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule schedule_window = auto @@ -374,7 +413,7 @@ search = `cloudtrail` eventName=RunInstances errorCode=success `abnormally_high_ 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"], "nist": ["DE.DP", "DE.AE"]} +action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "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. @@ -386,9 +425,9 @@ action.escu.full_search_name = ESCU - Abnormally High AWS Instances Terminated b action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious AWS EC2 Activities"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Abnormally High AWS Instances Terminated by User - Rule schedule_window = auto @@ -413,7 +452,7 @@ search = `cloudtrail` eventName=TerminateInstances errorCode=success | bucket sp 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"], "nist": ["DE.DP", "DE.AE"]} +action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "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. @@ -425,9 +464,9 @@ action.escu.full_search_name = ESCU - Abnormally High AWS Instances Terminated b action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious AWS EC2 Activities"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule schedule_window = auto @@ -465,9 +504,9 @@ action.escu.full_search_name = ESCU - Access LSASS Memory for Dump Creation - Ru action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Access LSASS Memory for Dump Creation - Rule schedule_window = auto @@ -505,9 +544,9 @@ action.escu.full_search_name = ESCU - Amazon EKS Kubernetes Pod scan detection - action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Kubernetes Scanning Activity"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Amazon EKS Kubernetes Pod scan detection - Rule schedule_window = auto @@ -544,9 +583,9 @@ action.escu.full_search_name = ESCU - Amazon EKS Kubernetes cluster scan detecti action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Kubernetes Scanning Activity"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Amazon EKS Kubernetes cluster scan detection - Rule schedule_window = auto @@ -583,9 +622,9 @@ action.escu.full_search_name = ESCU - Attempt To Add Certificate To Untrusted St action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Disabling Security Tools"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Attempt To Add Certificate To Untrusted Store - Rule schedule_window = auto @@ -605,7 +644,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 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)` | `attempt_to_add_certificate_to_untrusted_store_filter` +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 @@ -623,9 +662,9 @@ action.escu.full_search_name = ESCU - Attempt To Set Default PowerShell Executio action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Malicious PowerShell", "Credential Dumping"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule schedule_window = auto @@ -663,9 +702,9 @@ action.escu.full_search_name = ESCU - Attempt To Stop Security Service - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Disabling Security Tools"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Attempt To Stop Security Service - Rule schedule_window = auto @@ -703,9 +742,9 @@ action.escu.full_search_name = ESCU - Attempted Credential Dump From Registry vi action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Attempted Credential Dump From Registry via Reg exe - Rule schedule_window = auto @@ -743,9 +782,9 @@ action.escu.full_search_name = ESCU - Batch File Write to System32 - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["SamSam Ransomware"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Batch File Write to System32 - Rule schedule_window = auto @@ -783,9 +822,9 @@ action.escu.full_search_name = ESCU - Child Processes of Spoolsv exe - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Windows Privilege Escalation"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Child Processes of Spoolsv exe - Rule schedule_window = auto @@ -824,10 +863,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - Clients Connecting to Multiple DNS Servers - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Command and Control", "Host Redirection", "Suspicious DNS Traffic", "DNS Hijacking"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["DNS Hijacking", "Command and Control", "Suspicious DNS Traffic", "Host Redirection"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Clients Connecting to Multiple DNS Servers - Rule schedule_window = auto @@ -853,7 +892,7 @@ search = | tstats `security_content_summariesonly` count, values(DNS.dest) AS de action.escu = 0 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.mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1078"], "nist": ["ID.AM"]} action.escu.data_models = ["Cloud_Infrastructure"] 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. @@ -865,9 +904,9 @@ action.escu.full_search_name = ESCU - Cloud Compute Instance Created By Previous action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Cloud Cryptomining"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule schedule_window = auto @@ -905,9 +944,9 @@ action.escu.full_search_name = ESCU - Cloud Compute Instance Created With Previo action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Cloud Cryptomining"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule schedule_window = auto @@ -945,9 +984,9 @@ action.escu.full_search_name = ESCU - Cloud Compute Instance Created With Previo action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Cloud Cryptomining"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule schedule_window = auto @@ -973,7 +1012,7 @@ search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime values 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"], "nist": ["DE.DP", "DE.AE"]} +action.escu.mappings = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "nist": ["DE.DP", "DE.AE"]} action.escu.data_models = ["Cloud_Infrastructure"] 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. @@ -985,9 +1024,9 @@ action.escu.full_search_name = ESCU - Cloud Compute Instance Started In Previous action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Cloud Cryptomining"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule schedule_window = auto @@ -1029,9 +1068,9 @@ action.escu.full_search_name = ESCU - Common Ransomware Extensions - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["SamSam Ransomware", "Ransomware"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Common Ransomware Extensions - Rule schedule_window = auto @@ -1069,9 +1108,9 @@ action.escu.full_search_name = ESCU - Common Ransomware Notes - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["SamSam Ransomware", "Ransomware"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Common Ransomware Notes - Rule schedule_window = auto @@ -1091,7 +1130,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 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 @@ -1109,9 +1148,9 @@ action.escu.full_search_name = ESCU - Create Remote Thread into LSASS - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Create Remote Thread into LSASS - Rule schedule_window = auto @@ -1131,7 +1170,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 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] action.escu = 0 @@ -1149,9 +1188,9 @@ action.escu.full_search_name = ESCU - Create local admin accounts using net exe action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["DHS Report TA18-074A"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Create local admin accounts using net exe - Rule schedule_window = auto @@ -1171,7 +1210,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 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_accounts_using_net_exe_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] action.escu = 0 @@ -1189,9 +1228,9 @@ action.escu.full_search_name = ESCU - Create or delete windows shares using net action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Hidden Cobra Malware"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Create or delete windows shares using net exe - Rule schedule_window = auto @@ -1211,7 +1250,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 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_using_net_exe_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 @@ -1229,9 +1268,9 @@ action.escu.full_search_name = ESCU - Creation of Shadow Copy - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Creation of Shadow Copy - Rule schedule_window = auto @@ -1258,9 +1297,9 @@ 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": ["T1003"], "nist": ["DE.CM"]} -action.escu.data_models = [] +action.escu.data_models = ["Endpoint"] 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.how_to_implement = none 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 @@ -1269,14 +1308,14 @@ action.escu.full_search_name = ESCU - Creation of Shadow Copy with wmic and powe action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Creation of Shadow Copy with wmic and powershell - Rule schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['user', '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 @@ -1291,7 +1330,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 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` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic* OR Processes.process_name=powershell* Processes.process=*shadowcopy* Processes.process=*create* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `creation_of_shadow_copy_with_wmic_and_powershell_filter` [ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule] action.escu = 0 @@ -1309,9 +1348,9 @@ action.escu.full_search_name = ESCU - Credential Dumping via Copy Command from S action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule schedule_window = auto @@ -1349,9 +1388,9 @@ action.escu.full_search_name = ESCU - Credential Dumping via Symlink to Shadow C action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Credential Dumping via Symlink to Shadow Copy - Rule schedule_window = auto @@ -1394,10 +1433,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - DNS Query Length Outliers - MLTK - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Command and Control", "Hidden Cobra Malware", "Suspicious DNS Traffic"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Hidden Cobra Malware", "Suspicious DNS Traffic", "Command and Control"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - DNS Query Length Outliers - MLTK - Rule schedule_window = auto @@ -1434,10 +1473,10 @@ 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.providing_technologies = [] -action.escu.analytic_story = ["Command and Control", "Hidden Cobra Malware", "Suspicious DNS Traffic"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Hidden Cobra Malware", "Suspicious DNS Traffic", "Command and Control"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - DNS Query Length With High Standard Deviation - Rule schedule_window = auto @@ -1473,10 +1512,10 @@ 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.providing_technologies = [] -action.escu.analytic_story = ["Command and Control", "Host Redirection", "Suspicious DNS Traffic", "DNS Hijacking"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["DNS Hijacking", "Command and Control", "Suspicious DNS Traffic", "Host Redirection"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule schedule_window = auto @@ -1518,9 +1557,9 @@ action.escu.full_search_name = ESCU - DNS record changed - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["DNS Hijacking"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - DNS record changed - Rule schedule_window = auto @@ -1557,10 +1596,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - Deleting Shadow Copies - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["SamSam Ransomware", "Windows Log Manipulation", "Ransomware"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Windows Log Manipulation", "SamSam Ransomware", "Ransomware"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Deleting Shadow Copies - Rule schedule_window = auto @@ -1604,9 +1643,9 @@ action.escu.full_search_name = ESCU - Detect API activity from users without MFA action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS User Monitoring"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect API activity from users without MFA - Rule schedule_window = auto @@ -1632,7 +1671,7 @@ search = `cloudtrail` userIdentity.sessionContext.attributes.mfaAuthenticated=fa 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"], "nist": ["DE.DP", "DE.CM", "PR.AC", "ID.AM"]} +action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "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.\ @@ -1650,9 +1689,9 @@ action.escu.full_search_name = ESCU - Detect AWS API Activities From Unapproved action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS User Monitoring"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect AWS API Activities From Unapproved Accounts - Rule schedule_window = auto @@ -1678,7 +1717,7 @@ search = `cloudtrail` errorCode=success | rename userName as identity | search N 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"], "nist": ["DE.DP", "DE.AE"]} +action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "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. @@ -1689,10 +1728,10 @@ 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.providing_technologies = [] -action.escu.analytic_story = ["Suspicious AWS Login Activities"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Suspicious AWS Login Activities", "Suspicious Cloud Authentication Activities"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect AWS Console Login by User from New City - Rule schedule_window = auto @@ -1718,7 +1757,7 @@ search = | inputlookup previously_seen_users_console_logins.csv | stats min(firs 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"], "nist": ["DE.DP", "DE.AE"]} +action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "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. @@ -1729,10 +1768,10 @@ 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.providing_technologies = [] -action.escu.analytic_story = ["Suspicious AWS Login Activities"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Suspicious AWS Login Activities", "Suspicious Cloud Authentication Activities"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect AWS Console Login by User from New Country - Rule schedule_window = auto @@ -1758,7 +1797,7 @@ search = | inputlookup previously_seen_users_console_logins.csv | stats min(firs 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"], "nist": ["DE.DP", "DE.AE"]} +action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "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. @@ -1769,10 +1808,10 @@ 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.providing_technologies = [] -action.escu.analytic_story = ["Suspicious AWS Login Activities"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Suspicious AWS Login Activities", "Suspicious Cloud Authentication Activities"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect AWS Console Login by User from New Region - Rule schedule_window = auto @@ -1810,9 +1849,9 @@ action.escu.full_search_name = ESCU - Detect Activity Related to Pass the Hash A action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Lateral Movement"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect Activity Related to Pass the Hash Attacks - Rule schedule_window = auto @@ -1850,9 +1889,9 @@ action.escu.full_search_name = ESCU - Detect Credential Dumping through LSASS ac action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect Credential Dumping through LSASS access - Rule schedule_window = auto @@ -1872,7 +1911,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 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_LSAS`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 @@ -1894,9 +1933,9 @@ action.escu.full_search_name = ESCU - Detect DNS requests to Phishing Sites leve action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Common Phishing Frameworks"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect DNS requests to Phishing Sites leveraging EvilGinx2 - Rule schedule_window = auto @@ -1938,9 +1977,9 @@ action.escu.full_search_name = ESCU - Detect Excessive Account Lockouts From End action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Account Monitoring and Controls"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect Excessive Account Lockouts From Endpoint - Rule schedule_window = auto @@ -1978,9 +2017,9 @@ action.escu.full_search_name = ESCU - Detect Excessive User Account Lockouts - R action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Account Monitoring and Controls"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect Excessive User Account Lockouts - Rule schedule_window = auto @@ -2018,9 +2057,9 @@ action.escu.full_search_name = ESCU - Detect Large Outbound ICMP Packets - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Command and Control"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect Large Outbound ICMP Packets - Rule schedule_window = auto @@ -2056,10 +2095,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - Detect Long DNS TXT Record Response - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Command and Control", "Suspicious DNS Traffic"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Suspicious DNS Traffic", "Command and Control"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect Long DNS TXT Record Response - Rule schedule_window = auto @@ -2097,9 +2136,9 @@ action.escu.full_search_name = ESCU - Detect Mimikatz Using Loaded Images - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect Mimikatz Using Loaded Images - Rule schedule_window = auto @@ -2137,9 +2176,9 @@ action.escu.full_search_name = ESCU - Detect Mimikatz Via PowerShell And EventCo action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = [] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect Mimikatz Via PowerShell And EventCode 4703 - Rule schedule_window = auto @@ -2182,9 +2221,9 @@ action.escu.full_search_name = ESCU - Detect New Local Admin account - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["DHS Report TA18-074A"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect New Local Admin account - Rule schedule_window = auto @@ -2222,9 +2261,9 @@ action.escu.full_search_name = ESCU - Detect New Login Attempts to Routers - Rul action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Router and Infrastructure Security"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect New Login Attempts to Routers - Rule schedule_window = auto @@ -2250,7 +2289,7 @@ search = | tstats `security_content_summariesonly` count earliest(_time) as earl 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"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "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. @@ -2262,9 +2301,9 @@ action.escu.full_search_name = ESCU - Detect New Open S3 buckets - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious AWS S3 Activities"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect New Open S3 buckets - Rule schedule_window = auto @@ -2302,9 +2341,9 @@ action.escu.full_search_name = ESCU - Detect Oulook exe writing a zip file - Ru action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Phishing Payloads"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect Oulook exe writing a zip file - Rule schedule_window = auto @@ -2342,9 +2381,9 @@ action.escu.full_search_name = ESCU - Detect Outbound SMB Traffic - Rule action.escu.search_type = detection 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect Outbound SMB Traffic - Rule schedule_window = auto @@ -2381,9 +2420,9 @@ action.escu.full_search_name = ESCU - Detect Path Interception By Creation Of pr action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Windows Persistence Techniques"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect Path Interception By Creation Of program exe - Rule schedule_window = auto @@ -2420,10 +2459,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - Detect Prohibited Applications Spawning cmd exe - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Suspicious Command-Line Executions", "Suspicious MSHTA Activity"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Suspicious Command-Line Executions", "Suspicious MSHTA Activity", "Suspicious Zoom Child Processes"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect Prohibited Applications Spawning cmd exe - Rule schedule_window = auto @@ -2461,9 +2500,9 @@ action.escu.full_search_name = ESCU - Detect PsExec With accepteula Flag - Rule action.escu.search_type = detection 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect PsExec With accepteula Flag - Rule schedule_window = auto @@ -2501,9 +2540,9 @@ action.escu.full_search_name = ESCU - Detect Rare Executables - Rule action.escu.search_type = detection 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect Rare Executables - Rule schedule_window = auto @@ -2529,7 +2568,7 @@ search = | tstats `security_content_summariesonly` count values(Processes.dest) 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"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "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. @@ -2541,9 +2580,9 @@ action.escu.full_search_name = ESCU - Detect S3 access from a new IP - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious AWS S3 Activities"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect S3 access from a new IP - Rule schedule_window = auto @@ -2568,7 +2607,7 @@ search = `aws_s3_accesslogs` http_status=200 [search `aws_s3_accesslogs` http_s 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"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "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.\ @@ -2586,9 +2625,9 @@ action.escu.full_search_name = ESCU - Detect Spike in AWS API Activity - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS User Monitoring"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect Spike in AWS API Activity - Rule schedule_window = auto @@ -2626,9 +2665,9 @@ action.escu.full_search_name = ESCU - Detect Spike in Network ACL Activity - Rul action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Network ACL Activity"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect Spike in Network ACL Activity - Rule schedule_window = auto @@ -2654,7 +2693,7 @@ search = `cloudtrail` `network_acl_events` [search `cloudtrail` `network_acl_eve 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"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "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. @@ -2666,9 +2705,9 @@ action.escu.full_search_name = ESCU - Detect Spike in S3 Bucket deletion - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious AWS S3 Activities"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect Spike in S3 Bucket deletion - Rule schedule_window = auto @@ -2694,7 +2733,7 @@ search = `cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=Dele 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"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "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`. @@ -2706,9 +2745,9 @@ action.escu.full_search_name = ESCU - Detect Spike in Security Group Activity - action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS User Monitoring"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect Spike in Security Group Activity - Rule schedule_window = auto @@ -2745,10 +2784,10 @@ 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.providing_technologies = [] -action.escu.analytic_story = ["Command and Control", "AWS Network ACL Activity", "Suspicious AWS Traffic"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["AWS Network ACL Activity", "Suspicious AWS Traffic", "Command and Control"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule schedule_window = auto @@ -2785,9 +2824,9 @@ action.escu.full_search_name = ESCU - Detect USB device insertion - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Data Protection"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect USB device insertion - Rule schedule_window = auto @@ -2825,9 +2864,9 @@ action.escu.full_search_name = ESCU - Detect Unauthorized Assets by MAC address action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Asset Tracking"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect Unauthorized Assets by MAC address - Rule schedule_window = auto @@ -2863,10 +2902,10 @@ 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.providing_technologies = [] -action.escu.analytic_story = ["Suspicious Command-Line Executions", "Emotet Malware DHS Report TA18-201A "] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +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.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule schedule_window = auto @@ -2903,10 +2942,10 @@ 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.providing_technologies = [] -action.escu.analytic_story = ["SamSam Ransomware", "JBoss Vulnerability"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["JBoss Vulnerability", "SamSam Ransomware"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule schedule_window = auto @@ -2949,10 +2988,10 @@ 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.providing_technologies = [] -action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch", "Suspicious DNS Traffic", "DNS Hijacking", "Dynamic DNS", "Command and Control", "Data Protection"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Data Protection", "Prohibited Traffic Allowed or Protocol Mismatch", "DNS Hijacking", "Suspicious DNS Traffic", "Dynamic DNS", "Command and Control"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect hosts connecting to dynamic domain providers - Rule schedule_window = auto @@ -2988,10 +3027,10 @@ 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.providing_technologies = [] -action.escu.analytic_story = ["SamSam Ransomware", "JBoss Vulnerability"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["JBoss Vulnerability", "SamSam Ransomware"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect malicious requests to exploit JBoss servers - Rule schedule_window = auto @@ -3029,9 +3068,9 @@ action.escu.full_search_name = ESCU - Detect mshta exe running scripts in comman action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious MSHTA Activity"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect mshta exe running scripts in command-line arguments - Rule schedule_window = auto @@ -3057,7 +3096,7 @@ search = | tstats `security_content_summariesonly` count values(Processes.proces 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.mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1078", "T1098"], "nist": ["ID.AM"]} 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. @@ -3069,9 +3108,9 @@ action.escu.full_search_name = ESCU - Detect new API calls from user roles - Rul action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS User Monitoring"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect new API calls from user roles - Rule schedule_window = auto @@ -3097,7 +3136,7 @@ search = `cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=A 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"], "nist": ["DE.DP", "DE.AE"]} +action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "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. @@ -3109,9 +3148,9 @@ action.escu.full_search_name = ESCU - Detect new user AWS Console Login - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious AWS Login Activities"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect new user AWS Console Login - Rule schedule_window = auto @@ -3133,6 +3172,46 @@ realtime_schedule = 0 is_visible = false 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 new user AWS Console Login - DM - 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"], "nist": ["DE.DP", "DE.AE"]} +action.escu.data_models = ["Authentication"] +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 and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. 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 = 2020-05-28 +action.escu.modification_date = 2020-05-28 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Detect new user AWS Console Login - DM - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Suspicious Cloud Authentication Activities"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Detect new user AWS Console Login - DM - Rule +schedule_window = auto +action.notable = 1 +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 - DM +action.notable.param.security_domain = network +action.notable.param.severity = high +alert.digest_mode = 1 +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 +is_visible = false +search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user | `drop_dm_object_name(Authentication)` | 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)`| `detect_new_user_aws_console_login___dm_filter` + [ESCU - Detect processes used for System Network Configuration Discovery - Rule] action.escu = 0 action.escu.enabled = 1 @@ -3149,9 +3228,9 @@ action.escu.full_search_name = ESCU - Detect processes used for System Network C action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Unusual Processes"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect processes used for System Network Configuration Discovery - Rule schedule_window = auto @@ -3191,9 +3270,9 @@ action.escu.full_search_name = ESCU - Detect web traffic to dynamic domain provi action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Dynamic DNS"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detect web traffic to dynamic domain providers - Rule schedule_window = auto @@ -3230,10 +3309,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - Detection of DNS Tunnels - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Command and Control", "Data Protection", "Suspicious DNS Traffic"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Data Protection", "Suspicious DNS Traffic", "Command and Control"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detection of DNS Tunnels - Rule schedule_window = auto @@ -3271,9 +3350,9 @@ action.escu.full_search_name = ESCU - Detection of tools built by NirSoft - Rule action.escu.search_type = detection 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Detection of tools built by NirSoft - Rule schedule_window = auto @@ -3310,10 +3389,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - Disabling Remote User Account Control - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Defense Evasion Tactics"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Disabling Remote User Account Control - Rule schedule_window = auto @@ -3351,9 +3430,9 @@ action.escu.full_search_name = ESCU - Dump LSASS via comsvcs DLL - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Dump LSASS via comsvcs DLL - Rule schedule_window = auto @@ -3379,7 +3458,7 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime 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.mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1078"], "nist": ["ID.AM"]} 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`. @@ -3391,9 +3470,9 @@ action.escu.full_search_name = ESCU - EC2 Instance Modified With Previously Unse action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Unusual AWS EC2 Modifications"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - EC2 Instance Modified With Previously Unseen User - Rule schedule_window = auto @@ -3419,7 +3498,7 @@ search = `cloudtrail` `ec2_modification_api_calls` [search `cloudtrail` `ec2_mod 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"], "nist": ["DE.DP", "DE.AE"]} +action.escu.mappings = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078", "T1535"], "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. @@ -3431,9 +3510,9 @@ action.escu.full_search_name = ESCU - EC2 Instance Started In Previously Unseen action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - EC2 Instance Started In Previously Unseen Region - Rule schedule_window = auto @@ -3470,9 +3549,9 @@ action.escu.full_search_name = ESCU - EC2 Instance Started With Previously Unsee action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Cryptomining"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - EC2 Instance Started With Previously Unseen AMI - Rule schedule_window = auto @@ -3509,9 +3588,9 @@ action.escu.full_search_name = ESCU - EC2 Instance Started With Previously Unsee action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Cryptomining"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule schedule_window = auto @@ -3537,7 +3616,7 @@ search = `cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunI 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.mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1078"], "nist": ["ID.AM"]} 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. @@ -3549,9 +3628,9 @@ action.escu.full_search_name = ESCU - EC2 Instance Started With Previously Unsee action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - EC2 Instance Started With Previously Unseen User - Rule schedule_window = auto @@ -3590,10 +3669,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - Email Attachments With Lots Of Spaces - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Suspicious Emails", "Emotet Malware DHS Report TA18-201A "] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +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.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Email Attachments With Lots Of Spaces - Rule schedule_window = auto @@ -3630,9 +3709,9 @@ action.escu.full_search_name = ESCU - Email files written outside of the Outlook action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Collection and Staging"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Email files written outside of the Outlook directory - Rule schedule_window = auto @@ -3670,9 +3749,9 @@ action.escu.full_search_name = ESCU - Email servers sending high volume traffic action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Collection and Staging"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Email servers sending high volume traffic to hosts - Rule schedule_window = auto @@ -3708,10 +3787,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - Excessive DNS Failures - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Command and Control", "Suspicious DNS Traffic"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Suspicious DNS Traffic", "Command and Control"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Excessive DNS Failures - Rule schedule_window = auto @@ -3748,9 +3827,9 @@ action.escu.full_search_name = ESCU - Execution of File With Spaces Before Exten action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Windows File Extension and Association Abuse"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Execution of File With Spaces Before Extension - Rule schedule_window = auto @@ -3788,9 +3867,9 @@ action.escu.full_search_name = ESCU - Execution of File with Multiple Extensions action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Windows File Extension and Association Abuse"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Execution of File with Multiple Extensions - Rule schedule_window = auto @@ -3828,9 +3907,9 @@ action.escu.full_search_name = ESCU - Extended Period Without Successful Netback action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Monitor Backup Solution"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Extended Period Without Successful Netbackup Backups - Rule schedule_window = auto @@ -3868,9 +3947,9 @@ action.escu.full_search_name = ESCU - File with Samsam Extension - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["SamSam Ransomware"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - File with Samsam Extension - Rule schedule_window = auto @@ -3892,31 +3971,71 @@ realtime_schedule = 0 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 | `file_with_samsam_extension_filter` +[ESCU - First Time Seen Child Process of Zoom - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen. +action.escu.mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1068"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +action.escu.data_models = ["Endpoint"] +action.escu.eli5 = This search looks for child processes spawned by zoom.exe or zoom.us 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 should run the baseline search `Previously Seen Zoom Child Processes - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Zoom Child Processes - Update` to keep this table up to date and to age out old child processes. Please update the `previously_seen_zoom_child_processes_window` macro to adjust the time window. +action.escu.known_false_positives = A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken. +action.escu.creation_date = 2020-05-20 +action.escu.modification_date = 2020-05-20 +action.escu.confidence = high +action.escu.full_search_name = ESCU - First Time Seen Child Process of Zoom - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Suspicious Zoom Child Processes"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - First Time Seen Child Process of Zoom - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen. +action.notable.param.rule_title = First Time Seen Child Process of Zoom +action.notable.param.security_domain = endpoint +action.notable.param.severity = high +alert.digest_mode = 1 +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 +is_visible = false +search = | tstats `security_content_summariesonly` min(_time) as firstTime values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_id) as parent_process_id values(Processes.process_name) as process_name values(Processes.process) as process from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_id Processes.dest | `drop_dm_object_name(Processes)` | lookup zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") | `security_content_ctime(firstTime)` | table firstTime dest, process_id, process_name, parent_process_id, parent_process_name |`first_time_seen_child_process_of_zoom_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. +description = This search looks for the first and last time a Windows service is seen running in your environment. This table is then cached. 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.eli5 = This search looks for the first and last time a Windows service is seen running in your environment. This table is then cached. +action.escu.how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows system event logs in order for this search to execute successfully. You should run the baseline search `Previously Seen Running Windows Services - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Running Windows Services - Update` to keep this table up to date and to age out old Windows Services. Please update the `previously_seen_windows_service_window` macro to adjust the time window. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 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 = 2020-01-13 -action.escu.modification_date = 2020-01-13 +action.escu.creation_date = 2020-06-15 +action.escu.modification_date = 2020-06-15 action.escu.confidence = high action.escu.full_search_name = ESCU - First Time Seen Running Windows Service - Rule action.escu.search_type = detection 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - First Time Seen Running Windows Service - Rule schedule_window = auto action.notable = 1 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_description = This search looks for the first and last time a Windows service is seen running in your environment. This table is then cached. action.notable.param.rule_title = First Time Seen Running Windows Service action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -3930,7 +4049,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -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` +search = `wineventlog_system` EventCode=7036 | rex field=Message "The (?[-\(\)\s\w]+) service entered the (?\w+) state" | where state="running" | lookup previously_seen_running_windows_services service as service OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_windows_service_window`") | table _time dest service_name | `first_time_seen_running_windows_service_filter` [ESCU - First time seen command line argument - Rule] action.escu = 0 @@ -3947,10 +4066,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - First time seen command line argument - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Suspicious Command-Line Executions", "Hidden Cobra Malware", "DHS Report TA18-074A", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Orangeworm Attack Group"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +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 = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - First time seen command line argument - Rule schedule_window = auto @@ -3975,7 +4094,7 @@ search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_ 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 = {} +action.escu.mappings = {"mitre_attack": ["T1525"]} 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. @@ -3987,9 +4106,9 @@ action.escu.full_search_name = ESCU - GCP GCR container uploaded - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Container Implantation Monitoring and Investigation"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - GCP GCR container uploaded - Rule schedule_window = auto @@ -4027,9 +4146,9 @@ action.escu.full_search_name = ESCU - GCP Kubernetes cluster scan detection - Ru action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Kubernetes Scanning Activity"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - GCP Kubernetes cluster scan detection - Rule schedule_window = auto @@ -4065,15 +4184,15 @@ 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.providing_technologies = [] -action.escu.analytic_story = ["Windows Persistence Techniques", "Windows Defense Evasion Tactics"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Persistence Techniques"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Hiding Files And Directories With Attrib exe - Rule schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +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 @@ -4088,7 +4207,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 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)`| `hiding_files_and_directories_with_attrib_exe_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 @@ -4106,9 +4225,9 @@ action.escu.full_search_name = ESCU - Hosts receiving high volume of network tra action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Collection and Staging"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Hosts receiving high volume of network traffic from email server - Rule schedule_window = auto @@ -4145,9 +4264,9 @@ action.escu.full_search_name = ESCU - Identify New User Accounts - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Account Monitoring and Controls"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Identify New User Accounts - Rule schedule_window = auto @@ -4168,6 +4287,554 @@ realtime_schedule = 0 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 | `identify_new_user_accounts_filter` +[ESCU - Kerberoasting spn request with RC4 encryption - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search detects a potential kerberoasting attack via service principal name requests +action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1208"], "nist": ["DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search detects a potential kerberoasting attack via service principal name requests +action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, and include the windows security event logs that contain kerberos +action.escu.known_false_positives = Older systems that support kerberos RC4 by default NetApp may generate false positives +action.escu.creation_date = 2020-06-11 +action.escu.modification_date = 2020-06-11 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kerberoasting spn request with RC4 encryption - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Lateral Movement"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kerberoasting spn request with RC4 encryption - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search detects a potential kerberoasting attack via service principal name requests +action.notable.param.rule_title = Kerberoasting spn request with RC4 encryption +action.notable.param.security_domain = endpoint +action.notable.param.severity = high +alert.digest_mode = 1 +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 +is_visible = false +search = `wineventlog_security` EventID=4769 TicketOptions=0x40810000 TicketEncryptionType=0x17 | stats count min(_time) as firstTime max(_time) as lastTime values(ServiceName) values(TargetUserName) values(user) by TargetDomainName | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `kerberoasting_spn_request_with_rc4_encryption_filter` + +[ESCU - Kubernetes AWS detect RBAC authorization by account - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences +action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences +action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs +action.escu.known_false_positives = Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. +action.escu.creation_date = 2020-06-23 +action.escu.modification_date = 2020-06-23 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes AWS detect RBAC authorization by account - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Sensitive Role Activity"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes AWS detect RBAC authorization by account - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences +action.notable.param.rule_title = Kubernetes AWS detect RBAC authorization by account +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +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 +is_visible = false +search = `aws_cloudwatchlogs_eks` annotations.authorization.k8s.io/reason=* | table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason | stats count by user.username annotations.authorization.k8s.io/reason | rare user.username annotations.authorization.k8s.io/reason |`kubernetes_aws_detect_rbac_authorization_by_account_filter` + +[ESCU - Kubernetes AWS detect most active service accounts by pod - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision +action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision +action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs +action.escu.known_false_positives = Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness. +action.escu.creation_date = 2020-06-23 +action.escu.modification_date = 2020-06-23 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes AWS detect most active service accounts by pod - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Sensitive Role Activity"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes AWS detect most active service accounts by pod - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision +action.notable.param.rule_title = Kubernetes AWS detect most active service accounts by pod +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +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 +is_visible = false +search = `aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts objectRef.resource=pods | table sourceIPs{} user.username userAgent verb annotations.authorization.k8s.io/decision | top sourceIPs{} user.username verb annotations.authorization.k8s.io/decision |`kubernetes_aws_detect_most_active_service_accounts_by_pod_filter` + +[ESCU - Kubernetes AWS detect sensitive role access - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. +action.escu.known_false_positives = Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. +action.escu.creation_date = 2020-06-23 +action.escu.modification_date = 2020-06-23 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes AWS detect sensitive role access - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Sensitive Role Activity"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes AWS detect sensitive role access - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +action.notable.param.rule_title = Kubernetes AWS detect sensitive role access +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +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 +is_visible = false +search = `aws_cloudwatchlogs_eks` objectRef.resource=clusterroles OR clusterrolebindings sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1 | table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason | dedup user.username user.groups{} |`kubernetes_aws_detect_sensitive_role_access_filter` + +[ESCU - Kubernetes AWS detect service accounts forbidden failure access - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI +action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI +action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. +action.escu.known_false_positives = This search can give false positives as there might be inherent issues with authentications and permissions at cluster. +action.escu.creation_date = 2020-06-23 +action.escu.modification_date = 2020-06-23 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes AWS detect service accounts forbidden failure access - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Sensitive Object Access Activity"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes AWS detect service accounts forbidden failure access - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI +action.notable.param.rule_title = Kubernetes AWS detect service accounts forbidden failure access +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +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 +is_visible = false +search = `aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts responseStatus.status = Failure | table sourceIPs{} src_user userAgent verb responseStatus.status requestURI | `kubernetes_aws_detect_service_accounts_forbidden_failure_access_filter` + +[ESCU - Kubernetes AWS detect suspicious kubectl calls - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context +action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context +action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. +action.escu.known_false_positives = Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets +action.escu.creation_date = 2020-06-23 +action.escu.modification_date = 2020-06-23 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes AWS detect suspicious kubectl calls - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Sensitive Object Access Activity"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes AWS detect suspicious kubectl calls - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context +action.notable.param.rule_title = Kubernetes AWS detect suspicious kubectl calls +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +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 +is_visible = false +search = `aws_cloudwatchlogs_eks` userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 src_user=system:anonymous | table src_ip src_user verb userAgent requestURI | stats count by src_ip src_user verb userAgent requestURI |`kubernetes_aws_detect_suspicious_kubectl_calls_filter` + +[ESCU - Kubernetes Azure detect RBAC authorization by account - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences +action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences +action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +action.escu.known_false_positives = Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. +action.escu.creation_date = 2020-05-26 +action.escu.modification_date = 2020-05-26 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes Azure detect RBAC authorization by account - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Sensitive Role Activity"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes Azure detect RBAC authorization by account - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences +action.notable.param.rule_title = Kubernetes Azure detect RBAC authorization by account +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +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 +is_visible = false +search = sourcetype:mscs:storage:blob:json category=kube-audit | spath input=properties.log | search annotations.authorization.k8s.io/reason=* | table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason |stats count by user.username annotations.authorization.k8s.io/reason | rare user.username annotations.authorization.k8s.io/reason |`kubernetes_azure_detect_rbac_authorization_by_account_filter` + +[ESCU - Kubernetes Azure detect most active service accounts by pod namespace - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb +action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb +action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +action.escu.known_false_positives = Not all service accounts interactions are malicious. Analyst must consider IP and verb context when trying to detect maliciousness. +action.escu.creation_date = 2020-05-26 +action.escu.modification_date = 2020-05-26 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes Azure detect most active service accounts by pod namespace - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Sensitive Role Activity"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes Azure detect most active service accounts by pod namespace - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb +action.notable.param.rule_title = Kubernetes Azure detect most active service accounts by pod namespace +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +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 +is_visible = false +search = `kubernetes_azure` category=kube-audit | spath input=properties.log | search user.groups{}=system:serviceaccounts* OR user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow | table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace | top sourceIPs{} user.username verb responseStatus.status properties.pod objectRef.namespace |`kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace_filter` + +[ESCU - Kubernetes Azure detect sensitive object access - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +action.escu.known_false_positives = Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. +action.escu.creation_date = 2020-05-20 +action.escu.modification_date = 2020-05-20 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes Azure detect sensitive object access - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Sensitive Object Access Activity"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes Azure detect sensitive object access - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +action.notable.param.rule_title = Kubernetes Azure detect sensitive object access +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +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 +is_visible = false +search = `kubernetes_azure` category=kube-audit | spath input=properties.log| search objectRef.resource=secrets OR configmaps user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow |table user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason |dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_object_access_filter` + +[ESCU - Kubernetes Azure detect sensitive role access - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +action.escu.known_false_positives = Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. +action.escu.creation_date = 2020-05-20 +action.escu.modification_date = 2020-05-20 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes Azure detect sensitive role access - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Sensitive Role Activity"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes Azure detect sensitive role access - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +action.notable.param.rule_title = Kubernetes Azure detect sensitive role access +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +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 +is_visible = false +search = `kubernetes_azure` category=kube-audit | spath input=properties.log| search objectRef.resource=clusterroles OR clusterrolebindings | table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason | dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_role_access_filter` + +[ESCU - Kubernetes Azure detect service accounts forbidden failure access - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information on Kubernetes service accounts with failure or forbidden access status +action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information on Kubernetes service accounts with failure or forbidden access status +action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +action.escu.known_false_positives = This search can give false positives as there might be inherent issues with authentications and permissions at cluster. +action.escu.creation_date = 2020-05-20 +action.escu.modification_date = 2020-05-20 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes Azure detect service accounts forbidden failure access - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Sensitive Object Access Activity"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes Azure detect service accounts forbidden failure access - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search provides information on Kubernetes service accounts with failure or forbidden access status +action.notable.param.rule_title = Kubernetes Azure detect service accounts forbidden failure access +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +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 +is_visible = false +search = `kubernetes_azure` category=kube-audit | spath input=properties.log | search user.groups{}=system:serviceaccounts* responseStatus.reason=Forbidden | table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace |`kubernetes_azure_detect_service_accounts_forbidden_failure_access_filter` + +[ESCU - Kubernetes Azure detect suspicious kubectl calls - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information on rare Kubectl calls with IP, verb namespace and object access context +action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information on rare Kubectl calls with IP, verb namespace and object access context +action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +action.escu.known_false_positives = Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially suspicious IPs and sensitive objects such as configmaps or secrets +action.escu.creation_date = 2020-05-26 +action.escu.modification_date = 2020-05-26 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes Azure detect suspicious kubectl calls - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Sensitive Object Access Activity"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes Azure detect suspicious kubectl calls - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search provides information on rare Kubectl calls with IP, verb namespace and object access context +action.notable.param.rule_title = Kubernetes Azure detect suspicious kubectl calls +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +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 +is_visible = false +search = `kubernetes_azure` category=kube-audit | spath input=properties.log | spath input=responseObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration | search userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 | table sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI | rare sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI|`kubernetes_azure_detect_suspicious_kubectl_calls_filter` + +[ESCU - Kubernetes Azure pod scan fingerprint - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure +action.escu.mappings = {"kill_chain_phases": ["Reconnaissance"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure +action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +action.escu.known_false_positives = Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. +action.escu.creation_date = 2020-05-20 +action.escu.modification_date = 2020-05-20 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes Azure pod scan fingerprint - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Scanning Activity"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes Azure pod scan fingerprint - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure +action.notable.param.rule_title = Kubernetes Azure pod scan fingerprint +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +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 +is_visible = false +search = `kubernetes_azure` category=kube-audit | spath input=properties.log | search responseStatus.code=401 | table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod |`kubernetes_azure_pod_scan_fingerprint_filter` + +[ESCU - Kubernetes Azure scan fingerprint - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure +action.escu.mappings = {"kill_chain_phases": ["Reconnaissance"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure +action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +action.escu.known_false_positives = Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. +action.escu.creation_date = 2020-05-19 +action.escu.modification_date = 2020-05-19 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes Azure scan fingerprint - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Scanning Activity"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes Azure scan fingerprint - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure +action.notable.param.rule_title = Kubernetes Azure scan fingerprint +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +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 +is_visible = false +search = `kubernetes_azure` category=kube-audit | spath input=properties.log | search responseStatus.code=401 | table sourceIPs{} userAgent verb requestURI responseStatus.reason |`kubernetes_azure_scan_fingerprint_filter` + [ESCU - Large Volume of DNS ANY Queries - Rule] action.escu = 0 action.escu.enabled = 1 @@ -4184,9 +4851,9 @@ action.escu.full_search_name = ESCU - Large Volume of DNS ANY Queries - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["DNS Amplification Attacks"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Large Volume of DNS ANY Queries - Rule schedule_window = auto @@ -4223,9 +4890,9 @@ action.escu.full_search_name = ESCU - MacOS - Re-opened Applications - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = [] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - MacOS - Re-opened Applications - Rule schedule_window = auto @@ -4262,10 +4929,10 @@ 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.providing_technologies = [] -action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Malicious PowerShell"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +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 action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule schedule_window = auto @@ -4303,9 +4970,9 @@ action.escu.full_search_name = ESCU - Malicious PowerShell Process - Encoded Com action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Malicious PowerShell"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Malicious PowerShell Process - Encoded Command - Rule schedule_window = auto @@ -4343,9 +5010,9 @@ action.escu.full_search_name = ESCU - Malicious PowerShell Process - Execution P action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["DHS Report TA18-074A"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule schedule_window = auto @@ -4383,9 +5050,9 @@ action.escu.full_search_name = ESCU - Malicious PowerShell Process - Multiple Su action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Malicious PowerShell"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule schedule_window = auto @@ -4423,9 +5090,9 @@ action.escu.full_search_name = ESCU - Malicious PowerShell Process With Obfuscat action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Malicious PowerShell"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule schedule_window = auto @@ -4463,9 +5130,9 @@ action.escu.full_search_name = ESCU - Monitor DNS For Brand Abuse - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Brand Monitoring"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Monitor DNS For Brand Abuse - Rule schedule_window = auto @@ -4501,10 +5168,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - Monitor Email For Brand Abuse - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Suspicious Emails", "Brand Monitoring"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Brand Monitoring", "Suspicious Emails"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Monitor Email For Brand Abuse - Rule schedule_window = auto @@ -4541,9 +5208,9 @@ action.escu.full_search_name = ESCU - Monitor Registry Keys for Print Monitors - action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Persistence Techniques"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Monitor Registry Keys for Print Monitors - Rule schedule_window = auto @@ -4581,9 +5248,9 @@ action.escu.full_search_name = ESCU - Monitor Web Traffic For Brand Abuse - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Brand Monitoring"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Monitor Web Traffic For Brand Abuse - Rule schedule_window = auto @@ -4621,9 +5288,9 @@ action.escu.full_search_name = ESCU - Multiple Okta Users With Invalid Credentai action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious Okta Activity"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Multiple Okta Users With Invalid Credentails From The Same IP - Rule schedule_window = auto @@ -4648,7 +5315,7 @@ search = `okta` outcome.reason=INVALID_CREDENTIALS | rename client.geographicalC 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 = {} +action.escu.mappings = {"mitre_attack": ["T1525"]} 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. @@ -4660,9 +5327,9 @@ action.escu.full_search_name = ESCU - New container uploaded to AWS ECR - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Container Implantation Monitoring and Investigation"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - New container uploaded to AWS ECR - Rule schedule_window = auto @@ -4700,9 +5367,9 @@ action.escu.full_search_name = ESCU - No Windows Updates in a time frame - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Monitor for Updates"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - No Windows Updates in a time frame - Rule schedule_window = auto @@ -4740,9 +5407,9 @@ action.escu.full_search_name = ESCU - Okta Account Lockout Events - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious Okta Activity"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Okta Account Lockout Events - Rule schedule_window = auto @@ -4761,7 +5428,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -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` +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 @@ -4779,9 +5446,9 @@ action.escu.full_search_name = ESCU - Okta Failed SSO Attempts - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious Okta Activity"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Okta Failed SSO Attempts - Rule schedule_window = auto @@ -4800,7 +5467,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -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` +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 @@ -4818,9 +5485,9 @@ action.escu.full_search_name = ESCU - Okta User Logins From Multiple Cities - Ru action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious Okta Activity"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Okta User Logins From Multiple Cities - Rule schedule_window = auto @@ -4840,7 +5507,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -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 +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 @@ -4858,9 +5525,9 @@ action.escu.full_search_name = ESCU - Open Redirect in Splunk Web - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Splunk Enterprise Vulnerability"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Open Redirect in Splunk Web - Rule schedule_window = auto @@ -4897,9 +5564,9 @@ action.escu.full_search_name = ESCU - Osquery pack - ColdRoot detection - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["ColdRoot MacOS RAT"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Osquery pack - ColdRoot detection - Rule schedule_window = auto @@ -4936,9 +5603,9 @@ action.escu.full_search_name = ESCU - Overwriting Accessibility Binaries - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Windows Privilege Escalation"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Overwriting Accessibility Binaries - Rule schedule_window = auto @@ -4976,9 +5643,9 @@ action.escu.full_search_name = ESCU - Process Execution via WMI - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious WMI Use"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Process Execution via WMI - Rule schedule_window = auto @@ -5016,9 +5683,9 @@ action.escu.full_search_name = ESCU - Processes Tapping Keyboard Events - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["ColdRoot MacOS RAT"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Processes Tapping Keyboard Events - Rule schedule_window = auto @@ -5055,9 +5722,9 @@ action.escu.full_search_name = ESCU - Processes created by netsh - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Netsh Abuse"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Processes created by netsh - Rule schedule_window = auto @@ -5095,9 +5762,9 @@ action.escu.full_search_name = ESCU - Processes launching netsh - Rule action.escu.search_type = detection 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Processes launching netsh - Rule schedule_window = auto @@ -5134,10 +5801,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - Prohibited Network Traffic Allowed - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Command and Control", "Ransomware", "Prohibited Traffic Allowed or Protocol Mismatch"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Command and Control"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Prohibited Network Traffic Allowed - Rule schedule_window = auto @@ -5173,10 +5840,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - Prohibited Software On Endpoint - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["SamSam Ransomware", "Emotet Malware DHS Report TA18-201A ", "Monitor for Unauthorized Software"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Monitor for Unauthorized Software", "Emotet Malware DHS Report TA18-201A ", "SamSam Ransomware"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Prohibited Software On Endpoint - Rule schedule_window = auto @@ -5213,10 +5880,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - Protocol or Port Mismatch - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Command and Control", "Prohibited Traffic Allowed or Protocol Mismatch"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch", "Command and Control"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Protocol or Port Mismatch - Rule schedule_window = auto @@ -5253,9 +5920,9 @@ action.escu.full_search_name = ESCU - Protocols passing authentication in cleart action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Use of Cleartext Protocols"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Protocols passing authentication in cleartext - Rule schedule_window = auto @@ -5281,26 +5948,26 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime 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": ["T1050", "T1031", "T1089"], "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": ["T1058"], "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.how_to_implement = none 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 = 2019-03-01 -action.escu.modification_date = 2019-03-01 +action.escu.creation_date = 2020-06-15 +action.escu.modification_date = 2020-06-15 action.escu.confidence = high action.escu.full_search_name = ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Windows Persistence Techniques", "Windows Service Abuse"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Windows Service Abuse", "Windows Persistence Techniques"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['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 @@ -5315,7 +5982,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 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] | `reg_exe_manipulating_windows_services_registry_keys_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 values(Processes.user) as user FROM datamodel=Endpoint.Processes where Processes.process_name=reg.exe Processes.process=*reg* Processes.process=*add* Processes.process=*Services* by Processes.process_id Processes.dest Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `reg_exe_manipulating_windows_services_registry_keys_filter` [ESCU - Reg exe used to hide files directories via registry keys - Rule] action.escu = 0 @@ -5332,10 +5999,10 @@ 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.providing_technologies = [] -action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Persistence Techniques", "Windows Defense Evasion Tactics"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities", "Windows Persistence Techniques"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Reg exe used to hide files directories via registry keys - Rule schedule_window = auto @@ -5372,10 +6039,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - Registry Keys Used For Persistence - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Persistence Techniques", "Emotet Malware DHS Report TA18-201A ", "Ransomware", "DHS Report TA18-074A", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Suspicious MSHTA Activity"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +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 = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Registry Keys Used For Persistence - Rule schedule_window = auto @@ -5413,9 +6080,9 @@ action.escu.full_search_name = ESCU - Registry Keys Used For Privilege Escalatio action.escu.search_type = detection 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Registry Keys Used For Privilege Escalation - Rule schedule_window = auto @@ -5453,9 +6120,9 @@ action.escu.full_search_name = ESCU - Registry Keys for Creating SHIM Databases action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Persistence Techniques"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Registry Keys for Creating SHIM Databases - Rule schedule_window = auto @@ -5492,9 +6159,9 @@ action.escu.full_search_name = ESCU - Remote Desktop Network Bruteforce - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["SamSam Ransomware"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Remote Desktop Network Bruteforce - Rule schedule_window = auto @@ -5532,9 +6199,9 @@ action.escu.full_search_name = ESCU - Remote Desktop Network Traffic - Rule action.escu.search_type = detection 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Remote Desktop Network Traffic - Rule schedule_window = auto @@ -5572,9 +6239,9 @@ action.escu.full_search_name = ESCU - Remote Desktop Process Running On System - action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Hidden Cobra Malware", "Lateral Movement"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Remote Desktop Process Running On System - Rule schedule_window = auto @@ -5612,9 +6279,9 @@ action.escu.full_search_name = ESCU - Remote Process Instantiation via WMI - Rul action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Ransomware", "Suspicious WMI Use"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Remote Process Instantiation via WMI - Rule schedule_window = auto @@ -5651,10 +6318,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - Remote Registry Key modifications - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Persistence Techniques", "Windows Defense Evasion Tactics"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities", "Windows Persistence Techniques"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Remote Registry Key modifications - Rule schedule_window = auto @@ -5692,9 +6359,9 @@ action.escu.full_search_name = ESCU - Remote WMI Command Attempt - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious WMI Use"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Remote WMI Command Attempt - Rule schedule_window = auto @@ -5732,9 +6399,9 @@ action.escu.full_search_name = ESCU - RunDLL Loading DLL By Ordinal - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Unusual Processes"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - RunDLL Loading DLL By Ordinal - Rule schedule_window = auto @@ -5771,10 +6438,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - SMB Traffic Spike - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Ransomware", "Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "DHS Report TA18-074A"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "Ransomware", "DHS Report TA18-074A"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - SMB Traffic Spike - Rule schedule_window = auto @@ -5814,10 +6481,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - SMB Traffic Spike - MLTK - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Ransomware", "Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "DHS Report TA18-074A"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "Ransomware", "DHS Report TA18-074A"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - SMB Traffic Spike - MLTK - Rule schedule_window = auto @@ -5855,9 +6522,9 @@ action.escu.full_search_name = ESCU - SQL Injection with Long URLs - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["SQL Injection"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - SQL Injection with Long URLs - Rule schedule_window = auto @@ -5895,9 +6562,9 @@ action.escu.full_search_name = ESCU - Samsam Test File Write - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["SamSam Ransomware"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Samsam Test File Write - Rule schedule_window = auto @@ -5923,21 +6590,21 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime 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": ["T1050", "T1031", "T1089"], "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"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]} action.escu.data_models = ["Endpoint"] 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.how_to_implement = none 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 = 2019-02-27 -action.escu.modification_date = 2019-02-27 +action.escu.creation_date = 2020-06-15 +action.escu.modification_date = 2020-06-15 action.escu.confidence = high action.escu.full_search_name = ESCU - Sc exe Manipulating Windows Services - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Windows Persistence Techniques", "Windows Service Abuse", "Disabling Security Tools", "DHS Report TA18-074A", "Orangeworm Attack Group"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Windows Service Abuse", "DHS Report TA18-074A", "Orangeworm Attack Group", "Windows Persistence Techniques", "Disabling Security Tools"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Sc exe Manipulating Windows Services - Rule schedule_window = auto @@ -5975,9 +6642,9 @@ action.escu.full_search_name = ESCU - Scheduled Task Name Used by Dragonfly Thre action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["DHS Report TA18-074A"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule schedule_window = auto @@ -6015,9 +6682,9 @@ action.escu.full_search_name = ESCU - Scheduled tasks used in BadRabbit ransomwa action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Ransomware"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Scheduled tasks used in BadRabbit ransomware - Rule schedule_window = auto @@ -6055,9 +6722,9 @@ action.escu.full_search_name = ESCU - Schtasks scheduling job on remote system - action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Lateral Movement"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Schtasks scheduling job on remote system - Rule schedule_window = auto @@ -6095,9 +6762,9 @@ action.escu.full_search_name = ESCU - Schtasks used for forcing a reboot - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Windows Persistence Techniques", "Ransomware"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Schtasks used for forcing a reboot - Rule schedule_window = auto @@ -6135,9 +6802,9 @@ action.escu.full_search_name = ESCU - Script Execution via WMI - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious WMI Use"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Script Execution via WMI - Rule schedule_window = auto @@ -6175,9 +6842,9 @@ action.escu.full_search_name = ESCU - Shim Database File Creation - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Windows Persistence Techniques"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Shim Database File Creation - Rule schedule_window = auto @@ -6215,9 +6882,9 @@ action.escu.full_search_name = ESCU - Shim Database Installation With Suspicious action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Windows Persistence Techniques"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Shim Database Installation With Suspicious Parameters - Rule schedule_window = auto @@ -6255,9 +6922,9 @@ action.escu.full_search_name = ESCU - Short Lived Windows Accounts - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Account Monitoring and Controls"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Short Lived Windows Accounts - Rule schedule_window = auto @@ -6295,9 +6962,9 @@ action.escu.full_search_name = ESCU - Single Letter Process On Endpoint - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["DHS Report TA18-074A"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Single Letter Process On Endpoint - Rule schedule_window = auto @@ -6334,9 +7001,9 @@ action.escu.full_search_name = ESCU - Spectre and Meltdown Vulnerable Systems - action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Spectre And Meltdown Vulnerabilities"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Spectre and Meltdown Vulnerable Systems - Rule schedule_window = auto @@ -6373,9 +7040,9 @@ action.escu.full_search_name = ESCU - Spike in File Writes - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["SamSam Ransomware", "Ransomware"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Spike in File Writes - Rule schedule_window = auto @@ -6413,9 +7080,9 @@ action.escu.full_search_name = ESCU - Splunk Enterprise Information Disclosure - action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Splunk Enterprise Vulnerability CVE-2018-11409"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Splunk Enterprise Information Disclosure - Rule schedule_window = auto @@ -6453,9 +7120,9 @@ action.escu.full_search_name = ESCU - Suspicious Changes to File Associations - action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows File Extension and Association Abuse"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Suspicious Changes to File Associations - Rule schedule_window = auto @@ -6493,9 +7160,9 @@ action.escu.full_search_name = ESCU - Suspicious Email - UBA Anomaly - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious Emails"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Suspicious Email - UBA Anomaly - Rule schedule_window = auto @@ -6534,10 +7201,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - Suspicious Email Attachment Extensions - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Suspicious Emails", "Emotet Malware DHS Report TA18-201A "] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +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.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Suspicious Email Attachment Extensions - Rule schedule_window = auto @@ -6574,9 +7241,9 @@ action.escu.full_search_name = ESCU - Suspicious File Write - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Hidden Cobra Malware"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Suspicious File Write - Rule schedule_window = auto @@ -6614,9 +7281,9 @@ action.escu.full_search_name = ESCU - Suspicious Java Classes - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Apache Struts Vulnerability"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Suspicious Java Classes - Rule schedule_window = auto @@ -6654,9 +7321,9 @@ action.escu.full_search_name = ESCU - Suspicious LNK file launching a process - action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Phishing Payloads"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Suspicious LNK file launching a process - Rule schedule_window = auto @@ -6693,10 +7360,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - Suspicious Reg exe Process - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Disabling Security Tools", "Windows Defense Evasion Tactics", "DHS Report TA18-074A"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Disabling Security Tools", "DHS Report TA18-074A"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Suspicious Reg exe Process - Rule schedule_window = auto @@ -6734,9 +7401,9 @@ action.escu.full_search_name = ESCU - Suspicious wevtutil Usage - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Windows Log Manipulation", "Ransomware"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Suspicious wevtutil Usage - Rule schedule_window = auto @@ -6774,9 +7441,9 @@ action.escu.full_search_name = ESCU - Suspicious writes to System Volume Informa action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Collection and Staging"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Suspicious writes to System Volume Information - Rule schedule_window = auto @@ -6813,9 +7480,9 @@ action.escu.full_search_name = ESCU - Suspicious writes to windows Recycle Bin - action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Collection and Staging"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Suspicious writes to windows Recycle Bin - Rule schedule_window = auto @@ -6852,10 +7519,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - System Processes Run From Unexpected Locations - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Suspicious Command-Line Executions", "Ransomware", "Unusual Processes"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Suspicious Command-Line Executions", "Unusual Processes", "Ransomware"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - System Processes Run From Unexpected Locations - Rule schedule_window = auto @@ -6892,10 +7559,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - TOR Traffic - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Command and Control", "Ransomware", "Prohibited Traffic Allowed or Protocol Mismatch"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Command and Control"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - TOR Traffic - Rule schedule_window = auto @@ -6932,9 +7599,9 @@ action.escu.full_search_name = ESCU - USN Journal Deletion - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Windows Log Manipulation", "Ransomware"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - USN Journal Deletion - Rule schedule_window = auto @@ -6972,9 +7639,9 @@ action.escu.full_search_name = ESCU - Uncommon Processes On Endpoint - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Windows Privilege Escalation", "Unusual Processes"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Uncommon Processes On Endpoint - Rule schedule_window = auto @@ -7012,9 +7679,9 @@ action.escu.full_search_name = ESCU - Unload Sysmon Filter Driver - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Disabling Security Tools"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Unload Sysmon Filter Driver - Rule schedule_window = auto @@ -7052,9 +7719,9 @@ action.escu.full_search_name = ESCU - Unsigned Image Loaded by LSASS - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Unsigned Image Loaded by LSASS - Rule schedule_window = auto @@ -7092,9 +7759,9 @@ action.escu.full_search_name = ESCU - Unsuccessful Netbackup backups - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Monitor Backup Solution"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Unsuccessful Netbackup backups - Rule schedule_window = auto @@ -7130,10 +7797,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - Unusually Long Command Line - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Ransomware", "Suspicious Command-Line Executions", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Unusual Processes"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Suspicious Command-Line Executions", "Unusual Processes", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Unusually Long Command Line - Rule schedule_window = auto @@ -7153,7 +7820,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 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_command_line_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 @@ -7170,10 +7837,10 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - Unusually Long Command Line - MLTK - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Ransomware", "Suspicious Command-Line Executions", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Unusual Processes"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +action.escu.analytic_story = ["Suspicious Command-Line Executions", "Unusual Processes", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Unusually Long Command Line - MLTK - Rule schedule_window = auto @@ -7211,9 +7878,9 @@ action.escu.full_search_name = ESCU - Unusually Long Content-Type Length - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Apache Struts Vulnerability"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Unusually Long Content-Type Length - Rule schedule_window = auto @@ -7250,9 +7917,9 @@ action.escu.full_search_name = ESCU - WMI Permanent Event Subscription - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious WMI Use"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - WMI Permanent Event Subscription - Rule schedule_window = auto @@ -7290,9 +7957,9 @@ action.escu.full_search_name = ESCU - WMI Permanent Event Subscription - Sysmon action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious WMI Use"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - WMI Permanent Event Subscription - Sysmon - Rule schedule_window = auto @@ -7330,9 +7997,9 @@ action.escu.full_search_name = ESCU - WMI Temporary Event Subscription - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious WMI Use"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - WMI Temporary Event Subscription - Rule schedule_window = auto @@ -7369,9 +8036,9 @@ action.escu.full_search_name = ESCU - Web Fraud - Account Harvesting - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Web Fraud Detection"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Web Fraud - Account Harvesting - Rule schedule_window = auto @@ -7391,7 +8058,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -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` +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 @@ -7409,9 +8076,9 @@ action.escu.full_search_name = ESCU - Web Fraud - Anomalous User Clickspeed - Ru action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Web Fraud Detection"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Web Fraud - Anomalous User Clickspeed - Rule schedule_window = auto @@ -7448,9 +8115,9 @@ action.escu.full_search_name = ESCU - Web Fraud - Password Sharing Across Accoun action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Web Fraud Detection"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Web Fraud - Password Sharing Across Accounts - Rule schedule_window = auto @@ -7488,9 +8155,9 @@ action.escu.full_search_name = ESCU - Web Servers Executing Suspicious Processes action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Apache Struts Vulnerability"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Web Servers Executing Suspicious Processes - Rule schedule_window = auto @@ -7528,9 +8195,9 @@ action.escu.full_search_name = ESCU - Windows Event Log Cleared - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Windows Log Manipulation", "Ransomware"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Windows Event Log Cleared - Rule schedule_window = auto @@ -7568,9 +8235,9 @@ action.escu.full_search_name = ESCU - Windows hosts file modification - Rule action.escu.search_type = detection action.escu.providing_technologies = [] action.escu.analytic_story = ["Host Redirection"] -cron_schedule = */30 * * * * -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m action.correlationsearch.enabled = 1 action.correlationsearch.label = ESCU - Windows hosts file modification - Rule schedule_window = auto @@ -7597,7 +8264,7 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime ### ESCU BASELINES ### -[ESCU - Add Prohibited Processes to Enterprise Security - Baseline] +[ESCU - Add Prohibited Processes to Enterprise Security] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7607,8 +8274,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -7617,7 +8285,7 @@ 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] +[ESCU - Baseline of API Calls per User ARN] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7627,8 +8295,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -7637,7 +8306,7 @@ 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] +[ESCU - Baseline of Command Line Length - MLTK] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7647,8 +8316,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -7657,7 +8327,7 @@ 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] +[ESCU - Baseline of DNS Query Length - MLTK] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7667,8 +8337,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -7677,7 +8348,7 @@ 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] +[ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7687,8 +8358,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -7699,7 +8371,7 @@ 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] +[ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7709,8 +8381,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -7721,7 +8394,7 @@ 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] +[ESCU - Baseline of Network ACL Activity by ARN] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7731,8 +8404,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -7741,7 +8415,7 @@ 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] +[ESCU - Baseline of S3 Bucket deletion activity by ARN] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7751,8 +8425,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -7761,7 +8436,7 @@ 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] +[ESCU - Baseline of SMB Traffic - MLTK] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7771,8 +8446,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -7781,7 +8457,7 @@ 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] +[ESCU - Baseline of Security Group Activity by ARN] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7791,8 +8467,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -7801,7 +8478,7 @@ 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] +[ESCU - Baseline of blocked outbound traffic from AWS] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7811,8 +8488,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -7821,7 +8499,7 @@ 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] +[ESCU - Count of Unique IPs Connecting to Ports] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7831,8 +8509,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -7841,7 +8520,7 @@ 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] +[ESCU - Count of assets by category] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7851,8 +8530,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -7861,7 +8541,7 @@ 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] +[ESCU - Create a list of approved AWS service accounts] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7871,8 +8551,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -7881,7 +8562,7 @@ 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] +[ESCU - DNSTwist Domain Names] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7891,8 +8572,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -7901,7 +8583,7 @@ 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] +[ESCU - Discover DNS records] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7911,8 +8593,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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 @@ -7921,7 +8604,7 @@ 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] +[ESCU - Identify Systems Creating Remote Desktop Traffic] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7931,8 +8614,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m schedule_window = auto action.escu.providing_technologies = [] action.escu.eli5 = This search counts the numbers of times the system has generated remote desktop traffic. @@ -7941,7 +8625,7 @@ 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] +[ESCU - Identify Systems Receiving Remote Desktop Traffic] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7951,8 +8635,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m schedule_window = auto action.escu.providing_technologies = [] action.escu.eli5 = This search counts the numbers of times the system has created remote desktop traffic @@ -7961,7 +8646,7 @@ 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] +[ESCU - Identify Systems Using Remote Desktop] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7971,8 +8656,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -7981,7 +8667,7 @@ 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] +[ESCU - Monitor Successful Backups] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7991,8 +8677,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -8001,7 +8688,7 @@ 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] +[ESCU - Monitor Unsuccessful Backups] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8011,8 +8698,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -8021,7 +8709,7 @@ 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] +[ESCU - Previously Seen AWS Cross Account Activity] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8031,8 +8719,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -8041,7 +8730,7 @@ 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] +[ESCU - Previously Seen AWS Provisioning Activity Sources] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8051,8 +8740,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -8061,7 +8751,7 @@ 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] +[ESCU - Previously Seen AWS Regions] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8071,8 +8761,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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 @@ -8081,7 +8772,7 @@ 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] +[ESCU - Previously Seen Cloud Compute Creations By User] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8091,8 +8782,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -8101,7 +8793,7 @@ 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] +[ESCU - Previously Seen Cloud Compute Images] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8111,8 +8803,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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 @@ -8121,7 +8814,7 @@ 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] +[ESCU - Previously Seen Cloud Compute Instance Types] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8131,8 +8824,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m schedule_window = auto action.escu.providing_technologies = [] action.escu.eli5 = This search builds a table of previously seen cloud compute instance types @@ -8141,7 +8835,7 @@ 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] +[ESCU - Previously Seen Cloud Regions] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8151,8 +8845,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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 @@ -8161,7 +8856,7 @@ 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] +[ESCU - Previously Seen EC2 AMIs] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8171,8 +8866,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m schedule_window = auto action.escu.providing_technologies = [] action.escu.eli5 = This search builds a table of previously seen AMIs used to launch EC2 instances @@ -8181,7 +8877,7 @@ 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] +[ESCU - Previously Seen EC2 Instance Types] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8191,8 +8887,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m schedule_window = auto action.escu.providing_technologies = [] action.escu.eli5 = This search builds a table of previously seen EC2 instance types @@ -8201,7 +8898,7 @@ 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] +[ESCU - Previously Seen EC2 Launches By User] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8211,8 +8908,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -8221,7 +8919,7 @@ 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] +[ESCU - Previously Seen EC2 Modifications By User] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8231,8 +8929,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -8241,27 +8940,91 @@ 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] +[ESCU - Previously Seen Running Windows Services - Initial] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support -action.escu.full_search_name = ESCU - Previously Seen Running Windows Services +action.escu.full_search_name = ESCU - Previously Seen Running Windows Services - Initial 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.creation_date = 2020-06-23 +action.escu.modification_date = 2020-06-23 action.escu.analytic_story = ["Orangeworm Attack Group", "Windows Service Abuse"] action.escu.data_models = [] -dispatch.earliest_time = -30m -dispatch.latest_time = now +cron_schedule = 0 4 * * * +dispatch.earliest_time = -90d@d +dispatch.latest_time = -10m@m 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. +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 8.0.0 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 +search = `wineventlog_system` EventCode=7036 | rex field=Message "The (?[-\(\)\s\w]+) service entered the (?\w+) state" | where state="running" | stats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen by service | outputlookup previously_seen_running_windows_services -[ESCU - Previously seen API call per user roles in CloudTrail - Baseline] +[ESCU - Previously Seen Running Windows Services - Update] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously Seen Running Windows Services - Update +description = This search returns the first and last time a Windows service was seen across your enterprise within the last hour. It then updates this information with historical data and filters out Windows services pairs that have not been seen within the specified time window. This updated table is then cached. +action.escu.creation_date = 2020-06-23 +action.escu.modification_date = 2020-06-23 +action.escu.analytic_story = ["Orangeworm Attack Group", "Windows Service Abuse"] +action.escu.data_models = [] +cron_schedule = 55 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search returns the first and last time a Windows service was seen across your enterprise within the last hour. It then updates this information with historical data and filters out Windows services pairs that have not been seen within the specified time window. This updated table is then cached. +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 8.0.0 or above. +disabled = true +is_visible = false +search = `wineventlog_system` EventCode=7036 | rex field=Message "The (?[-\(\)\s\w]+) service entered the (?\w+) state" | where state="running" | stats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen by service | inputlookup previously_seen_running_windows_services append=t | stats min(firstTimeSeen) as firstTimeSeen, max(lastTimeSeen) as lastTimeSeen by service | where lastTimeSeen > relative_time(now(), "`previously_seen_windows_service_forget_window`") | outputlookup previously_seen_running_windows_services + +[ESCU - Previously Seen Zoom Child Processes - Initial] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously Seen Zoom Child Processes - Initial +description = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS). This table is then cached. +action.escu.creation_date = 2020-05-20 +action.escu.modification_date = 2020-05-20 +action.escu.analytic_story = ["Suspicious Zoom Child Processes"] +action.escu.data_models = ["Endpoint"] +cron_schedule = 0 4 * * * +dispatch.earliest_time = -90d@d +dispatch.latest_time = -10m@m +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS). This table is then cached. +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. +disabled = true +is_visible = false +search = | tstats `security_content_summariesonly` min(_time) as firstTimeSeen max(_time) as lastTimeSeen from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_name Processes.dest| `drop_dm_object_name(Processes)` | table dest, process_name, firstTimeSeen, lastTimeSeen | outputlookup zoom_first_time_child_process + +[ESCU - Previously Seen Zoom Child Processes - Update] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously Seen Zoom Child Processes - Update +description = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS) within the last hour. It then updates this information with historical data and filters out proces_name and endpoint pairs that have not been seen within the specified time window. This updated table is outputed to disk. +action.escu.creation_date = 2020-05-20 +action.escu.modification_date = 2020-05-20 +action.escu.analytic_story = ["Suspicious Zoom Child Processes"] +action.escu.data_models = ["Endpoint"] +cron_schedule = 55 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS) within the last hour. It then updates this information with historical data and filters out proces_name and endpoint pairs that have not been seen within the specified time window. This updated table is outputed to disk. +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. +disabled = true +is_visible = false +search = | tstats `security_content_summariesonly` min(_time) as firstTimeSeen max(_time) as lastTimeSeen from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_name Processes.dest| `drop_dm_object_name(Processes)` | table firstTimeSeen, lastTimeSeen, process_name, dest | inputlookup zoom_first_time_child_process append=t | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by process_name, dest | where lastTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_forget_window`") | outputlookup zoom_first_time_child_process + +[ESCU - Previously seen API call per user roles in CloudTrail] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8271,8 +9034,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -8281,7 +9045,7 @@ 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] +[ESCU - Previously seen S3 bucket access by remote IP] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8291,8 +9055,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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" @@ -8301,7 +9066,7 @@ 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] +[ESCU - Previously seen command line arguments] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8311,8 +9076,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -8321,7 +9087,7 @@ 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] +[ESCU - Previously seen users in CloudTrail] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8331,8 +9097,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -8341,7 +9108,28 @@ 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] +[ESCU - Previously seen users in CloudTrail - DM] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously seen users in CloudTrail - DM +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 username, within the last 30 days. +action.escu.creation_date = 2020-05-28 +action.escu.modification_date = 2020-05-28 +action.escu.analytic_story = ["Suspicious Cloud Authentication Activities"] +action.escu.data_models = ["Authentication"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +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 username, within the last 30 days. +action.escu.how_to_implement = You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Validate the user name entries in `previously_seen_users_console_logins.csv`, which is a lookup file created by this support search. +disabled = true +is_visible = false +search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | rename Authentication.user as user Authentication.src as src | table user src City Region Country firstTime lastTime | 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 @@ -8351,8 +9139,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -8361,7 +9150,7 @@ 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] +[ESCU - Update previously seen users in CloudTrail] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8371,8 +9160,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -8381,7 +9171,28 @@ 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] +[ESCU - Update previously seen users in CloudTrail - DM] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Update previously seen users in CloudTrail - DM +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 user, within the last hour. +action.escu.creation_date = 2020-05-28 +action.escu.modification_date = 2020-05-28 +action.escu.analytic_story = ["Suspicious Cloud Authentication Activities"] +action.escu.data_models = ["Authentication"] +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +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 user, within the last hour. +action.escu.how_to_implement = You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Validate the user name entries in `previously_seen_users_console_logins.csv`, which is a lookup file created by this support search. +disabled = true +is_visible = false +search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authenticaiton.src | iplocation Authentication.src | rename Authentication.user as user Authentciation.src as src | table user src City Region Country firstTime lastTime | 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 @@ -8391,8 +9202,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -8401,7 +9213,7 @@ 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] +[ESCU - Windows Updates Install Successes] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8411,8 +9223,9 @@ 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 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m 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. @@ -8425,6 +9238,27 @@ search = | tstats `security_content_summariesonly` dc(Updates.dest) as count FRO ### ESCU RESPONSE TASKS ### +[ESCU - AWS Investigate Security Hub alerts by dest - Response Task] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = investigative +action.escu.full_search_name = ESCU - AWS Investigate Security Hub alerts by dest +description = This search retrieves the all the alerts created by AWS Security Hub for a specific dest(instance_id). +action.escu.creation_date = 2020-06-08 +action.escu.modification_date = 2020-06-08 +action.escu.analytic_story = ["Cloud Compute Instance", "Cloud Cryptomining", "Suspicious AWS EC2 Activities", "AWS Suspicious Provisioning Activities"] +action.escu.earliest_time_offset = 3600 +action.escu.latest_time_offset = 86400 +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search retrieves the all the alerts created by AWS Security Hub for a specific dest(instance_id). +action.escu.how_to_implement = none +action.escu.known_false_positives = None at this time +disabled = true +schedule_window = auto +is_visible = false +search = sourcetype="aws:securityhub:firehose" "findings{}.Resources{}.Type"=AWSEC2Instance | rex field=findings{}.Resources{}.Id .*instance/(?.*) | search instance = $dest$ |rename findings{}.* as * | rename Remediation.Recommendation.Text as Remediation | table instance Title ProductArn Description FirstObservedAt RecordState Remediation + [ESCU - AWS Investigate User Activities By ARN - Response Task] action.escu = 0 action.escu.enabled = 1 @@ -8706,7 +9540,7 @@ 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 = 2017-11-01 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 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.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", "Suspicious Zoom Child Processes"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 action.escu.providing_technologies = [] @@ -8979,7 +9813,7 @@ 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 = 2017-09-20 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 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.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 Cloud Authentication Activities", "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 = [] @@ -9000,7 +9834,7 @@ 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 = 2017-09-20 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 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.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", "Kubernetes Sensitive Role Activity", "Kubernetes Sensitive Object Access Activity"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 action.escu.providing_technologies = [] @@ -9063,7 +9897,7 @@ action.escu.full_search_name = ESCU - Get Process File Activity description = This search returns the file activity for a specific process on a specific endpoint action.escu.creation_date = 2019-11-06 action.escu.modification_date = 2019-11-06 -action.escu.analytic_story = ["DHS Report TA18-074A"] +action.escu.analytic_story = ["DHS Report TA18-074A", "Suspicious Zoom Child Processes"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 action.escu.providing_technologies = [] @@ -9126,7 +9960,7 @@ action.escu.full_search_name = ESCU - Get Process Registry Activity description = This search returns the registry activity for a specific process on a specific endpoint action.escu.creation_date = 2019-11-06 action.escu.modification_date = 2019-11-06 -action.escu.analytic_story = ["DHS Report TA18-074A"] +action.escu.analytic_story = ["DHS Report TA18-074A", "Suspicious Zoom Child Processes"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 action.escu.providing_technologies = [] @@ -9357,7 +10191,7 @@ action.escu.full_search_name = ESCU - Investigate AWS User Activities by user fi 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. action.escu.creation_date = 2018-03-12 action.escu.modification_date = 2018-03-12 -action.escu.analytic_story = ["AWS User Monitoring"] +action.escu.analytic_story = ["AWS User Monitoring", "Suspicious Cloud Authentication Activities"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 action.escu.providing_technologies = [] diff --git a/package/default/transforms.conf b/package/default/transforms.conf index 9d23f65600..ec108d69fe 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: 2020-05-14T08:00:44 UTC +# On Date: 2020-06-26T13:32:24 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# @@ -153,8 +153,10 @@ filename = previously_seen_ec2_modifications_by_user.csv # description = A place holder for a list of AWS EC2 modifications done by each user [previously_seen_running_windows_services] -filename = previously_seen_running_windows_services.csv +collection = previously_seen_running_windows_services +external_type = kvstore # description = A placeholder for the list of Windows Services running +fields_list = _key, service, firstTimeSeen, lastTimeSeen [prohibitedProcesses_lookup] filename = prohibited_processes.csv @@ -200,3 +202,9 @@ default_match = false match_type = WILDCARD(file) min_matches = 1 +[zoom_first_time_child_process] +collection = zoom_first_time_child_process +external_type = kvstore +# description = A list of suspicious file names +fields_list = _key, dest, process_name, firstTimeSeen, lastTimeSeen + diff --git a/package/default/use_case_library.conf b/package/default/use_case_library.conf index 3880ef279b..38855e0838 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: 2020-05-14T08:00:44 UTC +# On Date: 2020-06-26T13:32:24 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# @@ -12,9 +12,9 @@ category = Cloud Security last_updated = 2018-06-04 version = 1 references = ["https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule", "ESCU - Get Notable History", "ESCU - AWS Investigate User Activities By AccessKeyId", "ESCU - AWS Investigate User Activities By Source User"] +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"] 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.\ @@ -25,9 +25,9 @@ category = Cloud Security last_updated = 2018-03-08 version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule", "ESCU - EC2 Instance Started With Previously Unseen AMI - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - Get Notable History", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Notable Info", "ESCU - Investigate AWS activities via region name", "ESCU - Get EC2 Launch Details"] +searches = ["ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule", "ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - EC2 Instance Started With Previously Unseen AMI - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Launch Details", "ESCU - Get User Information from Identity Table", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable History"] 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. \ @@ -39,9 +39,9 @@ category = Cloud Security last_updated = 2018-05-21 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 = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - Detect Spike in Network ACL Activity - Rule", "ESCU - AWS Network Access Control List Deleted - Rule", "ESCU - AWS Network Access Control List Created with All Open Ports - Rule", "ESCU - AWS Network ACL Details from ID", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio"] +searches = ["ESCU - AWS Network Access Control List Created with All Open Ports - Rule", "ESCU - Detect Spike in Network ACL Activity - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - AWS Network Access Control List Deleted - Rule", "ESCU - Get Process Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Risk Modifiers For User", "ESCU - Get All AWS Activity From IP Address", "ESCU - AWS Network ACL Details from ID", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get User Information from Identity Table", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. @@ -50,9 +50,9 @@ category = Cloud Security last_updated = 2018-03-16 version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule", "ESCU - Get All AWS Activity From City", "ESCU - Get All AWS Activity From Region", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get All AWS Activity From Country"] +searches = ["ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen City - 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 - AWS Investigate Security Hub alerts by dest", "ESCU - Get All AWS Activity From Region", "ESCU - Get All AWS Activity From IP Address"] 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. @@ -62,9 +62,9 @@ category = Cloud Security last_updated = 2018-03-12 version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf", "https://redlock.io/blog/cryptojacking-tesla"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] 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 - Investigate AWS User Activities by user field", "ESCU - Get Notable History", "ESCU - Get Notable Info"] +searches = ["ESCU - Detect AWS API Activities From Unapproved Accounts - Rule", "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 - Investigate AWS User Activities by user field", "ESCU - Get Notable Info", "ESCU - Get Notable History"] 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. \ @@ -75,10 +75,10 @@ The detection searches in this Analytic Story are designed to help you uncover A category = Best Practices last_updated = 2017-09-06 version = 1 -references = ["https://www.sans.org/media/critical-security-controls/critical-controls-poster-2016.pdf"] -maintainers = "David Dorsey, Splunk" +references = [] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Identify New User Accounts - Rule", "ESCU - Detect Excessive Account Lockouts From Endpoint - Rule", "ESCU - Short Lived Windows Accounts - Rule", "ESCU - Detect Excessive User Account Lockouts - Rule", "ESCU - Get Logon Rights Modifications For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Logon Rights Modifications For User"] +searches = ["ESCU - Short Lived Windows Accounts - Rule", "ESCU - Identify New User Accounts - Rule", "ESCU - Detect Excessive Account Lockouts From Endpoint - Rule", "ESCU - Detect Excessive User Account Lockouts - Rule", "ESCU - Get Logon Rights Modifications For User", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Logon Rights Modifications For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. @@ -87,9 +87,9 @@ category = Vulnerability last_updated = 2018-12-06 version = 1 references = ["https://github.com/SpiderLabs/owasp-modsecurity-crs/blob/v3.2/dev/rules/REQUEST-944-APPLICATION-ATTACK-JAVA.conf"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Web Servers Executing Suspicious Processes - Rule", "ESCU - Suspicious Java Classes - Rule", "ESCU - Unusually Long Content-Type Length - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web POSTs From src", "ESCU - Investigate Suspicious Strings in HTTP Header", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info"] +searches = ["ESCU - Web Servers Executing Suspicious Processes - Rule", "ESCU - Unusually Long Content-Type Length - Rule", "ESCU - Suspicious Java Classes - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Suspicious Strings in HTTP Header", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Investigate Web POSTs From src", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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.\ @@ -111,9 +111,9 @@ category = Best Practices last_updated = 2017-09-13 version = 1 references = ["https://www.cisecurity.org/controls/inventory-of-authorized-and-unauthorized-devices/"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -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"] +searches = ["ESCU - Detect Unauthorized Assets by MAC address - Rule", "ESCU - Get First Occurrence and Last Occurrence of a MAC Address", "ESCU - Get Notable Info", "ESCU - Get Notable History"] 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. @@ -122,9 +122,9 @@ category = Abuse last_updated = 2017-12-19 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 = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] 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 Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get User Information from Identity Table", "ESCU - Get Emails From Specific Sender", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Email Info"] +searches = ["ESCU - Monitor DNS For Brand Abuse - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Monitor Web Traffic For Brand Abuse - Rule", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get User Information from Identity Table", "ESCU - Get Email Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Emails From Specific Sender", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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.\ @@ -135,9 +135,9 @@ category = Cloud Security last_updated = 2019-10-02 version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["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 Created By Previously Unseen User - Rule", "ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule", "ESCU - Get Notable History", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Investigate User Activities In All Cloud Regions", "ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate Cloud Compute Instance Activities", "ESCU - Investigate User Activities In Single Cloud Region", "ESCU - Get Notable Info", "ESCU - Investigate AWS activities via region name", "ESCU - Get EC2 Launch Details"] +searches = ["ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule", "ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule", "ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Launch Details", "ESCU - Investigate User Activities In Single Cloud Region", "ESCU - AWS Investigate Security Hub alerts by dest", "ESCU - Investigate User Activities In All Cloud Regions", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Cloud Compute Instance Activities", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable History"] 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. \ @@ -149,9 +149,9 @@ category = Malware last_updated = 2019-01-09 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 = "Jose Hernandez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Jose Hernandez"}] spec_version = 3 -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 User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From src ip"] +searches = ["ESCU - Processes Tapping Keyboard Events - Rule", "ESCU - Osquery pack - ColdRoot detection - Rule", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get Risk Modifiers For User", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From src ip", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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.\ @@ -162,9 +162,9 @@ category = Adversary Tactics last_updated = 2020-02-03 version = 1 references = ["https://attack.mitre.org/wiki/Collection", "https://attack.mitre.org/wiki/Technique/T1074"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Suspicious writes to windows Recycle Bin - Rule", "ESCU - Email servers sending high volume traffic to hosts - Rule", "ESCU - Email files written outside of the Outlook directory - Rule", "ESCU - Hosts receiving high volume of network traffic from email server - Rule", "ESCU - Suspicious writes to System Volume Information - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +searches = ["ESCU - Suspicious writes to windows Recycle Bin - Rule", "ESCU - Suspicious writes to System Volume Information - Rule", "ESCU - Hosts receiving high volume of network traffic from email server - Rule", "ESCU - Email servers sending high volume traffic to hosts - Rule", "ESCU - Email files written outside of the Outlook directory - Rule", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. \ @@ -175,9 +175,9 @@ category = Adversary Tactics last_updated = 2018-06-01 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 = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Excessive DNS Failures - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - Detect Large Outbound ICMP Packets - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - AWS Network ACL Details from ID", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Parent Process Info"] +searches = ["ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - Detect Large Outbound ICMP Packets - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Get Process Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get All AWS Activity From IP Address", "ESCU - AWS Network ACL Details from ID", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get User Information from Identity Table", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. @@ -187,7 +187,7 @@ category = Adversary Tactics last_updated = 2019-04-29 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 = "Splunk Research Team, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Splunk Research Team"}] 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. @@ -199,9 +199,9 @@ category = Cloud Security last_updated = 2020-02-20 version = 1 references = ["https://github.com/splunk/cloud-datamodel-security-research"] -maintainers = "Rod Soto, Rico Valdez, Splunk" +maintainers = [{"company": "Rico Valdez, Splunk", "email": "-", "name": "Rod Soto"}] spec_version = 3 -searches = ["ESCU - New container uploaded to AWS ECR - Rule", "ESCU - GCP GCR container uploaded - Rule", "ESCU - Investigate AWS ECR container listing activity"] +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. @@ -210,9 +210,9 @@ category = Adversary Tactics last_updated = 2020-02-04 version = 3 references = ["https://attack.mitre.org/wiki/Technique/T1003", "https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Creation of Shadow Copy - Rule", "ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Access LSASS Memory for Dump Creation - Rule", "ESCU - Create Remote Thread into LSASS - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Credential Dumping via Symlink to Shadow Copy - Rule", "ESCU - Unsigned Image Loaded by LSASS - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule", "ESCU - Attempted Credential Dump From Registry via Reg exe - Rule", "ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Creation of Shadow Copy with wmic and powershell - Rule", "ESCU - Investigate Pass the Hash Attempts", "ESCU - Investigate Failed Logins for Multiple Destinations", "ESCU - Investigate Previous Unseen User", "ESCU - Investigate Pass the Ticket Attempts"] +searches = ["ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule", "ESCU - Creation of Shadow Copy - Rule", "ESCU - Access LSASS Memory for Dump Creation - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Creation of Shadow Copy with wmic and powershell - Rule", "ESCU - Credential Dumping via Symlink to 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 - Investigate Previous Unseen User", "ESCU - Investigate Pass the Hash Attempts", "ESCU - Investigate Failed Logins for Multiple Destinations", "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.\ @@ -223,9 +223,9 @@ category = Malware last_updated = 2020-01-22 version = 2 references = ["https://www.us-cert.gov/ncas/alerts/TA18-074A"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Single Letter Process On Endpoint - Rule", "ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Detect New Local Admin account - Rule", "ESCU - Create local admin accounts using net exe - Rule", "ESCU - Get Process Registry Activity", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Information For Port Activity", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get Process File Activity"] +searches = ["ESCU - Suspicious Reg exe Process - Rule", "ESCU - Create local admin accounts using net exe - Rule", "ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule", "ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule", "ESCU - Single Letter Process On Endpoint - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Detect New Local Admin account - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Registry Activity", "ESCU - Get Parent Process Info", "ESCU - Get Process File Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. \ @@ -237,9 +237,9 @@ category = Abuse last_updated = 2016-09-13 version = 1 references = ["https://www.us-cert.gov/ncas/alerts/TA13-088A", "https://www.imperva.com/learn/application-security/dns-amplification/"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Large Volume of DNS ANY Queries - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - Large Volume of DNS ANY Queries - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. @@ -249,9 +249,9 @@ category = Adversary Tactics last_updated = 2020-02-04 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 = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] 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"] +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Clients Connecting to Multiple 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. \ @@ -268,9 +268,9 @@ category = Abuse last_updated = 2017-09-14 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 = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Detection of DNS Tunnels - Rule", "ESCU - Detect USB device insertion - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio"] +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect USB device insertion - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get User Information from Identity Table", "ESCU - Get DNS Server History for a host", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. @@ -279,9 +279,9 @@ category = Adversary Tactics last_updated = 2020-02-04 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 = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Attempt To Add Certificate To Untrusted Store - Rule", "ESCU - Unload Sysmon Filter Driver - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Attempt To Stop Security Service - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +searches = ["ESCU - Unload Sysmon Filter Driver - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Attempt To Stop Security Service - Rule", "ESCU - Attempt To Add Certificate To Untrusted Store - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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). @@ -290,9 +290,9 @@ category = Malware last_updated = 2018-09-06 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 = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect web traffic to dynamic domain providers - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Investigate Web Activity From src ip"] +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect web traffic to dynamic domain providers - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From src ip", "ESCU - Get DNS Server History for a host", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. @@ -301,9 +301,9 @@ category = Malware last_updated = 2020-01-27 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 = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Detection of tools built by NirSoft - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Information For Port Activity", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Parent Process Info"] +searches = ["ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - Detection of tools built by NirSoft - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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.\ @@ -314,9 +314,9 @@ category = Malware last_updated = 2020-01-22 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 = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Suspicious File Write - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Create or delete windows shares using net exe - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get DNS traffic ratio", "ESCU - Get Parent Process Info"] +searches = ["ESCU - Remote Desktop Process Running On System - Rule", "ESCU - Suspicious File Write - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Create or delete windows shares using net exe - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Parent Process Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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.\ @@ -328,9 +328,9 @@ category = Abuse last_updated = 2017-09-14 version = 1 references = ["https://blog.malwarebytes.com/cybercrime/2016/09/hosts-file-hijacks/"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Windows hosts file modification - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host"] +searches = ["ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Windows hosts file modification - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get DNS Server History for a host", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. @@ -339,9 +339,9 @@ category = Vulnerability last_updated = 2017-09-14 version = 1 references = ["http://www.deependresearch.org/2016/04/jboss-exploits-view-from-victim.html"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Vulnerability Logs For Endpoint"] +searches = ["ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Investigate Web Activity From Host", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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.\ @@ -364,20 +364,42 @@ category = Cloud Security last_updated = 2020-04-15 version = 1 references = ["https://github.com/splunk/cloud-datamodel-security-research"] -maintainers = "Rod Soto, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rod Soto"}] spec_version = 3 -searches = ["ESCU - GCP Kubernetes cluster scan detection - Rule", "ESCU - Amazon EKS Kubernetes Pod scan detection - Rule", "ESCU - Amazon EKS Kubernetes cluster scan detection - Rule", "ESCU - GCP Kubernetes activity by src ip", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Amazon EKS Kubernetes activity by src ip", "ESCU - Get Notable Info"] +searches = ["ESCU - Amazon EKS Kubernetes Pod scan detection - Rule", "ESCU - GCP Kubernetes cluster scan detection - Rule", "ESCU - Kubernetes Azure pod scan fingerprint - Rule", "ESCU - Amazon EKS Kubernetes cluster scan detection - Rule", "ESCU - Kubernetes Azure scan fingerprint - Rule", "ESCU - Amazon EKS Kubernetes activity by src ip", "ESCU - GCP Kubernetes activity by src ip", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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://Kubernetes Sensitive Object Access Activity] +category = Cloud Security +last_updated = 2020-05-20 +version = 1 +references = ["https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rod Soto"}] +spec_version = 3 +searches = ["ESCU - Kubernetes AWS detect service accounts forbidden failure access - Rule", "ESCU - Kubernetes Azure detect suspicious kubectl calls - Rule", "ESCU - Kubernetes AWS detect suspicious kubectl calls - Rule", "ESCU - Kubernetes Azure detect sensitive object access - Rule", "ESCU - AWS EKS Kubernetes cluster sensitive object access - Rule", "ESCU - Kubernetes Azure detect service accounts forbidden failure access - Rule", "ESCU - Get Notable Info"] +description = This story addresses detection and response of accounts acccesing Kubernetes cluster sensitive objects such as configmaps or secrets providing information on items such as user user, group. object, namespace and authorization reason. +narrative = Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive objects within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes sensitive objects. + +[analytic_story://Kubernetes Sensitive Role Activity] +category = Cloud Security +last_updated = 2020-05-20 +version = 1 +references = ["https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rod Soto"}] +spec_version = 3 +searches = ["ESCU - Kubernetes AWS detect most active service accounts by pod - Rule", "ESCU - Kubernetes AWS detect sensitive role access - Rule", "ESCU - Kubernetes Azure detect sensitive role access - Rule", "ESCU - Kubernetes Azure detect most active service accounts by pod namespace - Rule", "ESCU - Kubernetes Azure detect RBAC authorization by account - Rule", "ESCU - Kubernetes AWS detect RBAC authorization by account - Rule", "ESCU - Get Notable Info"] +description = This story addresses detection and response around Sensitive Role usage within a Kubernetes clusters against cluster resources and namespaces. +narrative = Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive roles within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes role activities + [analytic_story://Lateral Movement] category = Adversary Tactics last_updated = 2020-02-04 version = 2 -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 = "David Dorsey, Splunk" +references = ["https://www.fireeye.com/blog/executive-perspective/2015/08/malware_lateral_move.html"] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Remote Desktop Process Running On System - Rule", "ESCU - Detect Activity Related to Pass the Hash Attacks - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Schtasks scheduling job on remote system - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +searches = ["ESCU - Remote Desktop Process Running On System - Rule", "ESCU - Detect Activity Related to Pass the Hash Attacks - Rule", "ESCU - Kerberoasting spn request with RC4 encryption - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Schtasks scheduling job on remote system - Rule", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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.\ @@ -390,9 +412,9 @@ category = Adversary Tactics last_updated = 2017-08-23 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 = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Malicious PowerShell Process - Encoded Command - Rule", "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 - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +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 - Encoded Command - Rule", "ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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: \ @@ -410,9 +432,9 @@ category = Best Practices last_updated = 2017-09-12 version = 1 references = ["https://www.carbonblack.com/2016/03/04/tracking-locky-ransomware-using-carbon-black/"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Unsuccessful Netbackup backups - Rule", "ESCU - Extended Period Without Successful Netbackup Backups - Rule", "ESCU - All backup logs for host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - Unsuccessful Netbackup backups - Rule", "ESCU - Extended Period Without Successful Netbackup Backups - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - All backup logs for host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. @@ -421,9 +443,9 @@ category = Best Practices last_updated = 2017-09-15 version = 1 references = ["https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Parent Process Info"] +searches = ["ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. @@ -433,9 +455,9 @@ category = Best Practices last_updated = 2017-09-15 version = 1 references = ["https://learn.cisecurity.org/20-controls-download"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - No Windows Updates in a time frame - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - No Windows Updates in a time frame - Rule", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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.\ @@ -445,10 +467,10 @@ Microsoft releases updates for Windows systems on a monthly cadence. They should category = Abuse last_updated = 2017-01-05 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 = "Bhavin Patel, Splunk" +references = ["https://docs.microsoft.com/en-us/previous-versions/tn-archive/bb490939(v=technet.10)", "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": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Processes launching netsh - Rule", "ESCU - Processes created by netsh - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +searches = ["ESCU - Processes launching netsh - Rule", "ESCU - Processes created by netsh - Rule", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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`. @@ -458,9 +480,9 @@ category = Malware last_updated = 2020-01-22 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 = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - First time seen command line argument - Rule", "ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +searches = ["ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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.\ @@ -473,7 +495,7 @@ category = Adversary Tactics last_updated = 2019-04-29 version = 1 references = ["https://www.fireeye.com/blog/threat-research/2019/04/spear-phishing-campaign-targets-ukraine-government.html"] -maintainers = "Splunk Research Team, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Splunk Research Team"}] spec_version = 3 searches = ["ESCU - Suspicious LNK file launching a process - Rule", "ESCU - Detect Oulook exe writing a zip file - 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. @@ -491,9 +513,9 @@ category = Adversary Tactics last_updated = 2020-01-22 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 = "iDefense Cyber Espionage Team, iDefense" +maintainers = [{"company": "iDefense", "email": "-", "name": "iDefense Cyber Espionage Team"}] 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 Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +searches = ["ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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.\ @@ -529,9 +551,9 @@ category = Best Practices last_updated = 2017-09-11 version = 1 references = ["http://www.novetta.com/2015/02/advanced-methods-to-detect-advanced-cyber-attacks-protocol-abuse/"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - TOR Traffic - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Parent Process Info"] +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 Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get DNS Server History for a host", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. @@ -540,9 +562,9 @@ category = Malware last_updated = 2020-02-04 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 = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Scheduled tasks used in BadRabbit ransomware - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Information For Port Activity", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Parent Process Info"] +searches = ["ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - Scheduled tasks used in BadRabbit ransomware - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. @@ -551,9 +573,9 @@ category = Best Practices last_updated = 2017-09-12 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 = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Detect New Login Attempts to Routers - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info"] +searches = ["ESCU - Detect New Login Attempts to Routers - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. @@ -563,9 +585,9 @@ category = Adversary Tactics last_updated = 2017-09-19 version = 1 references = ["https://capec.mitre.org/data/definitions/66.html", "https://www.incapsula.com/web-application-security/sql-injection.html"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] 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"] +searches = ["ESCU - SQL Injection with Long URLs - Rule", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. @@ -575,9 +597,9 @@ category = Malware last_updated = 2018-12-13 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 = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - File with Samsam Extension - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Remote Desktop Network Bruteforce - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Samsam Test File Write - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Batch File Write to System32 - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Parent Process Info"] +searches = ["ESCU - Prohibited Software On Endpoint - Rule", "ESCU - File with Samsam Extension - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Samsam Test File Write - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Remote Desktop Network Bruteforce - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Batch File Write to System32 - Rule", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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.\ @@ -591,9 +613,9 @@ category = Vulnerability last_updated = 2018-01-08 version = 1 references = ["https://meltdownattack.com/"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Spectre and Meltdown Vulnerable Systems - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info"] +searches = ["ESCU - Spectre and Meltdown Vulnerable Systems - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. @@ -602,9 +624,9 @@ category = Vulnerability last_updated = 2017-09-19 version = 1 references = ["http://www.splunk.com/view/SP-CAAAPQ6#announce", "https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2016-4859"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Open Redirect in Splunk Web - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - Open Redirect in Splunk Web - Rule", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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:\ \ @@ -622,9 +644,9 @@ category = Vulnerability last_updated = 2018-06-14 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 = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Splunk Enterprise Information Disclosure - Rule", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get Notable Info", "ESCU - Investigate Web Activity From src ip"] +searches = ["ESCU - Splunk Enterprise Information Disclosure - Rule", "ESCU - Investigate Network Traffic From src ip", "ESCU - Investigate Web Activity From src ip", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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.\ @@ -636,9 +658,9 @@ category = Cloud Security last_updated = 2018-02-09 version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - Rule", "ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - Get Notable History", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Notable Info", "ESCU - Investigate AWS activities via region name", "ESCU - Get EC2 Launch Details"] +searches = ["ESCU - EC2 Instance Started With Previously Unseen User - 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 - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Launch Details", "ESCU - AWS Investigate Security Hub alerts by dest", "ESCU - Get User Information from Identity Table", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable History"] 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. @@ -647,9 +669,9 @@ category = Cloud Security last_updated = 2019-05-01 version = 1 references = ["https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Detect new user AWS Console Login - Rule", "ESCU - Detect AWS Console Login by User from New Region - Rule", "ESCU - Detect AWS Console Login by User from New City - Rule", "ESCU - Detect AWS Console Login by User from New Country - Rule", "ESCU - AWS Investigate User Activities By ARN"] +searches = ["ESCU - Detect AWS Console Login by User from New Country - Rule", "ESCU - Detect new user AWS Console Login - Rule", "ESCU - Detect AWS Console Login by User from New Region - Rule", "ESCU - Detect AWS Console Login by User from New City - 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. @@ -658,9 +680,9 @@ category = Cloud Security last_updated = 2018-07-24 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 = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -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 - Get Notable History", "ESCU - AWS S3 Bucket details via bucketName", "ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable Info", "ESCU - Investigate AWS activities via region name"] +searches = ["ESCU - Detect S3 access from a new IP - Rule", "ESCU - Detect New Open S3 buckets - Rule", "ESCU - Detect Spike in S3 Bucket deletion - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get User Information from Identity Table", "ESCU - AWS S3 Bucket details via bucketName", "ESCU - Get Notable Info", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable History"] 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.\ @@ -671,23 +693,35 @@ category = Cloud Security last_updated = 2018-05-07 version = 1 references = ["https://rhinosecuritylabs.com/aws/hiding-cloudcobalt-strike-beacon-c2-using-amazon-apis/"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - AWS Network ACL Details from ID", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio"] +searches = ["ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - Get Process Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Risk Modifiers For User", "ESCU - Get All AWS Activity From IP Address", "ESCU - AWS Network ACL Details from ID", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get User Information from Identity Table", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. +[analytic_story://Suspicious Cloud Authentication Activities] +category = Cloud Security +last_updated = 2020-06-04 +version = 1 +references = ["https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/", "https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Detect AWS Console Login by User from New Country - Rule", "ESCU - Detect AWS Console Login by User from New Region - Rule", "ESCU - Detect AWS Console Login by User from New City - Rule", "ESCU - Detect new user AWS Console Login - DM - Rule", "ESCU - Investigate AWS User Activities by user field", "ESCU - Get Notable History"] +description = Monitor your cloud authentication events. Searches within this Analytic Story leverage the recent cloud updates to the Authentication data model to help you stay aware of and investigate suspicious login activity. +narrative = It is important to monitor and control who has access to your cloud infrastructure. Detecting suspicious logins 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 compute activity whether legitimate or otherwise.\ +This Analytic Story has data model versions of cloud searches leveraging Authentication data, including those looking for suspicious login activity, and cross-account activity for AWS. + [analytic_story://Suspicious Command-Line Executions] category = Adversary Tactics last_updated = 2020-02-03 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 = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +searches = ["ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - System Processes Run From Unexpected Locations - 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 Process Info", "ESCU - Get Registry Activities", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. @@ -696,9 +730,9 @@ category = Adversary Tactics last_updated = 2017-09-18 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 = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Excessive DNS Failures - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Parent Process Info"] +searches = ["ESCU - Detection of DNS Tunnels - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get User Information from Identity Table", "ESCU - Get DNS Server History for a host", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. @@ -707,9 +741,9 @@ category = Adversary Tactics last_updated = 2020-01-27 version = 1 references = ["https://www.splunk.com/blog/2015/06/26/phishing-hits-a-new-level-of-quality/"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Suspicious Email - UBA Anomaly - Rule", "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 Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get User Information from Identity Table", "ESCU - Get Emails From Specific Sender", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Email Info"] +searches = ["ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Suspicious Email - UBA Anomaly - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Email Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Emails From Specific Sender", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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: \ @@ -722,9 +756,9 @@ category = Adversary Tactics last_updated = 2020-02-03 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 = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] 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 Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +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 Process Info", "ESCU - Get Registry Activities", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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).\ @@ -735,9 +769,9 @@ category = Adversary Tactics last_updated = 2020-04-02 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 = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Okta User Logins From Multiple Cities - Rule", "ESCU - Okta Account Lockout Events - Rule", "ESCU - Okta Failed SSO Attempts - Rule", "ESCU - Multiple Okta Users With Invalid Credentails From The Same IP - Rule", "ESCU - Investigate Okta Activity by app", "ESCU - Investigate Okta Activity by IP Address", "ESCU - Investigate User Activities In Okta"] +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 User Activities In Okta", "ESCU - Investigate Okta Activity by app", "ESCU - Investigate Okta Activity by IP Address"] 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. \ @@ -748,9 +782,9 @@ category = Adversary Tactics last_updated = 2018-10-23 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 = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Process Execution via WMI - Rule", "ESCU - WMI Permanent Event Subscription - Sysmon - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - Script Execution via WMI - Rule", "ESCU - Remote WMI Command Attempt - Rule", "ESCU - WMI Temporary Event Subscription - Rule", "ESCU - WMI Permanent Event Subscription - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Parent Process Info"] +searches = ["ESCU - Remote WMI Command Attempt - Rule", "ESCU - Process Execution via WMI - Rule", "ESCU - WMI Temporary Event Subscription - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - WMI Permanent Event Subscription - Sysmon - Rule", "ESCU - WMI Permanent Event Subscription - Rule", "ESCU - Script Execution via WMI - Rule", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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.\ @@ -761,22 +795,34 @@ category = Adversary Tactics last_updated = 2018-05-31 version = 1 references = ["https://redcanary.com/blog/windows-registry-attacks-threat-detection/", "https://attack.mitre.org/wiki/Technique/T1112"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Disabling Remote User Account Control - Rule", "ESCU - Monitor Registry Keys for Print Monitors - 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 - Suspicious Changes to File Associations - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +searches = ["ESCU - Remote Registry Key modifications - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Disabling Remote User Account Control - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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.\ The searches in this story are designed to help you detect behaviors associated with manipulation of the Windows registry. +[analytic_story://Suspicious Zoom Child Processes] +category = Adversary Tactics +last_updated = 2020-04-13 +version = 1 +references = ["https://blog.rapid7.com/2020/04/02/dispelling-zoom-bugbears-what-you-need-to-know-about-the-latest-zoom-vulnerabilities/", "https://threatpost.com/two-zoom-zero-day-flaws-uncovered/154337/"] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - First Time Seen Child Process of Zoom - Rule", "ESCU - Get Process Registry Activity", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process File Activity"] +description = Attackers are using Zoom as an vector to increase privileges on a sytems. This story detects new child processes of zoom and provides investigative actions for this detection. +narrative = Zoom is a leader in modern enterprise video communications and its usage has increased dramatically with a large amount of the population under stay-at-home orders due to the COVID-19 pandemic. With increased usage has come increased scrutiny and several security flaws have been found with this application on both Windows and macOS systems.\ +Current detections focus on finding new child processes of this application on a per host basis. Investigative searches are included to gather information needed during an investigation. + [analytic_story://Unusual AWS EC2 Modifications] category = Cloud Security last_updated = 2018-04-09 version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - EC2 Instance Modified With Previously Unseen User - Rule", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable History", "ESCU - AWS Investigate User Activities By ARN"] +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"] 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. @@ -786,9 +832,9 @@ category = Malware last_updated = 2020-02-04 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 = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Detect processes used for System Network Configuration Discovery - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - RunDLL Loading DLL By Ordinal - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +searches = ["ESCU - RunDLL Loading DLL By Ordinal - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Detect processes used for System Network Configuration Discovery - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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.\ @@ -799,9 +845,9 @@ category = Best Practices last_updated = 2017-09-15 version = 1 references = ["https://www.monkey.org/~dugsong/dsniff/"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Protocols passing authentication in cleartext - Rule", "ESCU - Get Notable History", "ESCU - Get Process Information For Port Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info"] +searches = ["ESCU - Protocols passing authentication in cleartext - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. @@ -810,9 +856,9 @@ category = Abuse last_updated = 2018-10-08 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 = "Jim Apger, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Jim Apger"}] spec_version = 3 -searches = ["ESCU - Web Fraud - Account Harvesting - Rule", "ESCU - Web Fraud - Anomalous User Clickspeed - Rule", "ESCU - Web Fraud - Password Sharing Across Accounts - Rule", "ESCU - Get Web Session Information via session id", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Emails From Specific Sender"] +searches = ["ESCU - Web Fraud - Password Sharing Across Accounts - Rule", "ESCU - Web Fraud - Anomalous User Clickspeed - Rule", "ESCU - Web Fraud - Account Harvesting - Rule", "ESCU - Get Notable Info", "ESCU - Get Emails From Specific Sender", "ESCU - Get Web Session Information via session id", "ESCU - Get Notable History"] 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.\ @@ -826,9 +872,9 @@ category = Adversary Tactics last_updated = 2018-05-31 version = 1 references = ["https://attack.mitre.org/wiki/Defense_Evasion"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Disabling Remote User Account Control - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +searches = ["ESCU - Remote Registry Key modifications - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Disabling Remote User Account Control - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. @@ -837,9 +883,9 @@ category = Malware last_updated = 2018-01-26 version = 1 references = ["https://blog.malwarebytes.com/cybercrime/2013/12/file-extensions-2/", "https://attack.mitre.org/wiki/Technique/T1042"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Execution of File with Multiple Extensions - Rule", "ESCU - Execution of File With Spaces Before Extension - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +searches = ["ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Execution of File With Spaces Before Extension - Rule", "ESCU - Execution of File with Multiple Extensions - Rule", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. \ @@ -852,9 +898,9 @@ category = Adversary Tactics last_updated = 2017-09-12 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 = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Suspicious wevtutil Usage - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Parent Process Info"] +searches = ["ESCU - USN Journal Deletion - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Get Process Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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). @@ -864,9 +910,9 @@ category = Adversary Tactics last_updated = 2018-05-31 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 = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Shim Database File Creation - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Detect Path Interception By Creation Of program exe - Rule", "ESCU - Shim Database Installation With Suspicious Parameters - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +searches = ["ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Detect Path Interception By Creation Of program exe - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Shim Database Installation With Suspicious Parameters - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Shim Database File Creation - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. @@ -875,9 +921,9 @@ category = Adversary Tactics last_updated = 2020-02-04 version = 2 references = ["https://attack.mitre.org/tactics/TA0004/"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Overwriting Accessibility Binaries - Rule", "ESCU - Child Processes of Spoolsv exe - 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 - Investigate Web Activity From Host", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +searches = ["ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Overwriting Accessibility Binaries - Rule", "ESCU - Child Processes of Spoolsv exe - Rule", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. @@ -886,9 +932,9 @@ category = Malware last_updated = 2017-11-02 version = 3 references = ["https://attack.mitre.org/wiki/Technique/T1050", "https://attack.mitre.org/wiki/Technique/T1031"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -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 User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info"] +searches = ["ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History"] 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. @@ -898,211 +944,221 @@ narrative = The Windows operating system uses a services architecture to allow f [savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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"]} +annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1535"], "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 = none +providing_technologies = [] [savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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"]} +annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1535"], "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 = none +providing_technologies = [] [savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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 = none +providing_technologies = [] [savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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"]} +annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1535"], "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 = none +providing_technologies = [] [savedsearch://ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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"], "nist": ["PR.AC", "PR.DS", "DE.AE"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "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 = none +providing_technologies = [] + +[savedsearch://ESCU - AWS EKS Kubernetes cluster sensitive object access - Rule] +type = detection +asset_type = AWS EKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets +how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. +providing_technologies = [] [savedsearch://ESCU - AWS Network Access Control List Created with All Open Ports - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - AWS Network Access Control List Deleted - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = It's possible that a user has legitimately deleted a network ACL. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Abnormally High AWS Instances Launched by User - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Abnormally High AWS Instances Terminated by User - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Access LSASS Memory for Dump Creation - Rule] type = detection -asset_type = +asset_type = Windows 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Amazon EKS Kubernetes Pod scan detection - Rule] type = detection -asset_type = +asset_type = Amazon EKS Kubernetes cluster Pod confidence = medium 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"]} known_false_positives = Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Amazon EKS Kubernetes cluster scan detection - Rule] type = detection -asset_type = +asset_type = Amazon EKS Kubernetes cluster 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"]} known_false_positives = Not all unauthenticated requests are malicious, but frequency, UA and source IPs will provide context. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Attempt To Add Certificate To Untrusted Store - Rule] type = detection -asset_type = +asset_type = Endpoint 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule] type = detection -asset_type = +asset_type = Endpoint 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Attempt To Stop Security Service - Rule] type = detection -asset_type = +asset_type = Endpoint 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Attempted Credential Dump From Registry via Reg exe - Rule] type = detection -asset_type = +asset_type = Endpoint 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": ["T1003"], "nist": ["DE.CM"]} known_false_positives = None identified. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Batch File Write to System32 - Rule] type = detection -asset_type = +asset_type = Endpoint 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Child Processes of Spoolsv exe - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Clients Connecting to Multiple DNS Servers - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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.\ @@ -1110,51 +1166,51 @@ This search produces fields (`dest_count`) that are not yet supported by ES Inci 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule] type = detection -asset_type = +asset_type = Cloud Compute Instance confidence = medium 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"]} +annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule] type = detection -asset_type = +asset_type = Cloud Compute Instance confidence = medium 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 = none +providing_technologies = [] [savedsearch://ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule] type = detection -asset_type = +asset_type = Cloud Compute Instance confidence = medium 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 = none +providing_technologies = [] [savedsearch://ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule] type = detection -asset_type = +asset_type = Cloud Compute Instance confidence = medium 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"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Common Ransomware Extensions - Rule] type = detection -asset_type = +asset_type = Endpoint 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.\ @@ -1164,91 +1220,91 @@ This search produces fields (`query`,`query_length`,`count`) that are not yet su 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Common Ransomware Notes - Rule] type = detection -asset_type = +asset_type = Endpoint 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Create Remote Thread into LSASS - Rule] type = detection -asset_type = +asset_type = Windows 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Create local admin accounts using net exe - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Administrators often leverage net.exe to create admin accounts. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Create or delete windows shares using net exe - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Creation of Shadow Copy - Rule] type = detection -asset_type = +asset_type = Endpoint 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": ["T1003"], "nist": ["DE.CM"]} known_false_positives = Legtimate administrator usage of Ntdsutil, Vssadmin, or Wmic will create false positives. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Creation of Shadow Copy with wmic and powershell - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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. +how_to_implement = none 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 = none +providing_technologies = [] [savedsearch://ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule] type = detection -asset_type = +asset_type = Endpoint 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": ["T1003"], "nist": ["DE.CM"]} known_false_positives = unknown -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Credential Dumping via Symlink to Shadow Copy - Rule] type = detection -asset_type = +asset_type = Endpoint 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": ["T1003"], "nist": ["DE.CM"]} known_false_positives = unknown -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - DNS Query Length Outliers - MLTK - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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.\ @@ -1260,31 +1316,31 @@ This search produces fields (`query`,`query_length`,`count`) that are not yet su 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - DNS Query Length With High Standard Deviation - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - DNS record changed - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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". \ @@ -1294,21 +1350,21 @@ If Splunk>Phantom is also configured in your environment, a Playbook called "DNS 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 = none +providing_technologies = [] [savedsearch://ESCU - Deleting Shadow Copies - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Detect API activity from users without MFA - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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.\ @@ -1320,11 +1376,11 @@ This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Detect AWS API Activities From Unapproved Accounts - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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.\ @@ -1334,63 +1390,63 @@ 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"], "nist": ["DE.DP", "DE.CM", "PR.AC", "ID.AM"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Detect AWS Console Login by User from New City - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Detect AWS Console Login by User from New Country - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Detect AWS Console Login by User from New Region - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Detect Activity Related to Pass the Hash Attacks - Rule] type = detection -asset_type = +asset_type = Endpoint 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Detect Credential Dumping through LSASS access - Rule] type = detection -asset_type = +asset_type = Windows confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Detect DNS requests to Phishing Sites leveraging EvilGinx2 - Rule] type = detection -asset_type = +asset_type = Endpoint 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. \ @@ -1400,11 +1456,11 @@ If Splunk>Phantom is also configured in your environment, a Playbook called `Let 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 = none +providing_technologies = [] [savedsearch://ESCU - Detect Excessive Account Lockouts From Endpoint - Rule] type = detection -asset_type = +asset_type = Windows 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. \ @@ -1414,61 +1470,61 @@ If Splunk>Phantom is also configured in your environment, a Playbook called "Exc 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 = none +providing_technologies = [] [savedsearch://ESCU - Detect Excessive User Account Lockouts - Rule] type = detection -asset_type = +asset_type = Windows confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Detect Large Outbound ICMP Packets - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Detect Long DNS TXT Record Response - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Detect Mimikatz Using Loaded Images - Rule] type = detection -asset_type = +asset_type = Windows 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Detect Mimikatz Via PowerShell And EventCode 4703 - Rule] type = detection -asset_type = +asset_type = Windows confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Detect New Local Admin account - Rule] type = detection -asset_type = +asset_type = Windows confidence = medium 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.\ @@ -1480,47 +1536,47 @@ This search produces fields (`Security_ID`,`Group_Name`,`Message`) that are not 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Detect New Login Attempts to Routers - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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 = none +providing_technologies = [] [savedsearch://ESCU - Detect New Open S3 buckets - Rule] type = detection -asset_type = +asset_type = S3 Bucket confidence = medium 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"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Detect Oulook exe writing a zip file - Rule] type = detection -asset_type = +asset_type = Endpoint 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Detect Outbound SMB Traffic - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Detect Path Interception By Creation Of program exe - Rule] type = detection @@ -1530,51 +1586,51 @@ explanation = The search is looking for the creation of program.exe in the C: dr 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Detect Prohibited Applications Spawning cmd exe - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Detect PsExec With accepteula Flag - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Detect Rare Executables - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Detect S3 access from a new IP - Rule] type = detection -asset_type = +asset_type = S3 Bucket 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"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +annotations = {"cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Detect Spike in AWS API Activity - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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.\ @@ -1584,93 +1640,93 @@ 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"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} known_false_positives = -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Spike in Network ACL Activity - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Detect Spike in S3 Bucket deletion - Rule] type = detection -asset_type = +asset_type = S3 Bucket confidence = medium 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"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Detect Spike in Security Group Activity - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Detect USB device insertion - Rule] type = detection -asset_type = +asset_type = Endpoint 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"], "nist": ["PR.PT", "PR.DS"]} known_false_positives = Legitimate USB activity will also be detected. Please verify and investigate as appropriate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Unauthorized Assets by MAC address - Rule] type = detection -asset_type = +asset_type = Infrastructure confidence = medium 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Some legitimate applications may exhibit this behavior. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule] type = detection -asset_type = +asset_type = Web Server confidence = medium 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": ["T1082"]} known_false_positives = It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect hosts connecting to dynamic domain providers - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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`.\ @@ -1682,61 +1738,71 @@ This search produces fields (query, answer, isDynDNS) that are not yet supported 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Detect malicious requests to exploit JBoss servers - Rule] type = detection -asset_type = +asset_type = Web Server 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Detect mshta exe running scripts in command-line arguments - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect new API calls from user roles - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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"]} +annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078", "T1098"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Detect new user AWS Console Login - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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": ["T1078"], "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 = [] + +[savedsearch://ESCU - Detect new user AWS Console Login - DM - Rule] +type = detection +asset_type = AWS Instance +confidence = medium +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 and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Detect processes used for System Network Configuration Discovery - Rule] type = detection -asset_type = +asset_type = Endpoint 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Detect web traffic to dynamic domain providers - Rule] type = detection -asset_type = +asset_type = Endpoint 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.\ @@ -1744,101 +1810,101 @@ This search produces fields (`isDynDNS`) that are not yet supported by ES Incide 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Detection of DNS Tunnels - Rule] type = detection -asset_type = +asset_type = Endpoint 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Detection of tools built by NirSoft - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Disabling Remote User Account Control - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Dump LSASS via comsvcs DLL - Rule] type = detection -asset_type = +asset_type = Endpoint 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": ["T1003"], "nist": ["DE.CM"]} known_false_positives = None identified. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - EC2 Instance Modified With Previously Unseen User - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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"]} +annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078"], "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 = none +providing_technologies = [] [savedsearch://ESCU - EC2 Instance Started In Previously Unseen Region - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078", "T1535"], "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 = none +providing_technologies = [] [savedsearch://ESCU - EC2 Instance Started With Previously Unseen AMI - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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 = none +providing_technologies = [] [savedsearch://ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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 = none +providing_technologies = [] [savedsearch://ESCU - EC2 Instance Started With Previously Unseen User - Rule] type = detection -asset_type = +asset_type = AWS Instance confidence = medium 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"]} +annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Email Attachments With Lots Of Spaces - Rule] type = detection -asset_type = +asset_type = Endpoint 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. \ @@ -1846,117 +1912,127 @@ how_to_implement = You need to ingest data from emails. Specifically, the sender 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"], "nist": ["PR.IP"]} known_false_positives = None at this time -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Email files written outside of the Outlook directory - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Email servers sending high volume traffic to hosts - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Excessive DNS Failures - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Execution of File With Spaces Before Extension - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["T1042"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} known_false_positives = None identified. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Execution of File with Multiple Extensions - Rule] type = detection -asset_type = +asset_type = Endpoint 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": ["T1042"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} known_false_positives = None identified. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Extended Period Without Successful Netbackup Backups - Rule] type = detection -asset_type = +asset_type = Endpoint 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 = none +providing_technologies = [] [savedsearch://ESCU - File with Samsam Extension - Rule] type = detection -asset_type = +asset_type = Endpoint 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"], "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 = none +providing_technologies = [] + +[savedsearch://ESCU - First Time Seen Child Process of Zoom - Rule] +type = detection +asset_type = Endpoint +confidence = medium +explanation = This search looks for child processes spawned by zoom.exe or zoom.us 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 should run the baseline search `Previously Seen Zoom Child Processes - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Zoom Child Processes - Update` to keep this table up to date and to age out old child processes. Please update the `previously_seen_zoom_child_processes_window` macro to adjust the time window. +annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1068"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +known_false_positives = A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken. +providing_technologies = [] [savedsearch://ESCU - First Time Seen Running Windows Service - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium -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. +explanation = This search looks for the first and last time a Windows service is seen running in your environment. This table is then cached. +how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows system event logs in order for this search to execute successfully. You should run the baseline search `Previously Seen Running Windows Services - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Running Windows Services - Update` to keep this table up to date and to age out old Windows Services. Please update the `previously_seen_windows_service_window` macro to adjust the time window. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above. 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 = none +providing_technologies = [] [savedsearch://ESCU - First time seen command line argument - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - GCP GCR container uploaded - Rule] type = detection -asset_type = +asset_type = GCP GCR Container confidence = medium 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 = {} +annotations = {"mitre_attack": ["T1525"]} 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 = none +providing_technologies = [] [savedsearch://ESCU - GCP Kubernetes cluster scan detection - Rule] type = detection -asset_type = +asset_type = GCP Kubernetes cluster 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"]} known_false_positives = Not all unauthenticated requests are malicious, but frequency, User Agent and source IPs will provide context. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Hiding Files And Directories With Attrib exe - Rule] type = detection @@ -1966,317 +2042,457 @@ explanation = Attackers leverage an existing Windows binary, attrib.exe, to mark 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"], "nist": ["DE.CM"]} known_false_positives = Some applications and users may legitimately use attrib.exe to interact with the files. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Hosts receiving high volume of network traffic from email server - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Identify New User Accounts - Rule] type = detection -asset_type = +asset_type = Domain Server confidence = medium 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": ["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 = none +providing_technologies = [] + +[savedsearch://ESCU - Kerberoasting spn request with RC4 encryption - Rule] +type = detection +asset_type = Endpoint +confidence = medium +explanation = This search detects a potential kerberoasting attack via service principal name requests +how_to_implement = You must be ingesting endpoint data that tracks process activity, and include the windows security event logs that contain kerberos +annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1208"], "nist": ["DE.CM"]} +known_false_positives = Older systems that support kerberos RC4 by default NetApp may generate false positives +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes AWS detect RBAC authorization by account - Rule] +type = detection +asset_type = AWS EKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences +how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes AWS detect most active service accounts by pod - Rule] +type = detection +asset_type = AWS EKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision +how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes AWS detect sensitive role access - Rule] +type = detection +asset_type = AWS EKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes AWS detect service accounts forbidden failure access - Rule] +type = detection +asset_type = AWS EKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI +how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = This search can give false positives as there might be inherent issues with authentications and permissions at cluster. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes AWS detect suspicious kubectl calls - Rule] +type = detection +asset_type = AWS EKS Kubernetes cluster +confidence = medium +explanation = This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context +how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs. +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect RBAC authorization by account - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect most active service accounts by pod namespace - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Not all service accounts interactions are malicious. Analyst must consider IP and verb context when trying to detect maliciousness. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect sensitive object access - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect sensitive role access - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect service accounts forbidden failure access - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes service accounts with failure or forbidden access status +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = This search can give false positives as there might be inherent issues with authentications and permissions at cluster. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect suspicious kubectl calls - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on rare Kubectl calls with IP, verb namespace and object access context +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially suspicious IPs and sensitive objects such as configmaps or secrets +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure pod scan fingerprint - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Reconnaissance"]} +known_false_positives = Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure scan fingerprint - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Reconnaissance"]} +known_false_positives = Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. +providing_technologies = [] [savedsearch://ESCU - Large Volume of DNS ANY Queries - Rule] type = detection -asset_type = +asset_type = DNS Servers 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 = none +providing_technologies = [] [savedsearch://ESCU - MacOS - Re-opened Applications - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Malicious PowerShell Process - Encoded Command - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Monitor DNS For Brand Abuse - Rule] type = detection -asset_type = +asset_type = Endpoint 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 = none +providing_technologies = [] [savedsearch://ESCU - Monitor Email For Brand Abuse - Rule] type = detection -asset_type = +asset_type = Endpoint 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 = none +providing_technologies = [] [savedsearch://ESCU - Monitor Registry Keys for Print Monitors - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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"], "nist": ["PR.PT", "DE.CM", "PR.AC"]} known_false_positives = You will encounter noise from legitimate print-monitor registry entries. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Monitor Web Traffic For Brand Abuse - Rule] type = detection -asset_type = +asset_type = Endpoint 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"], "nist": ["PR.IP"]} known_false_positives = None at this time -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Multiple Okta Users With Invalid Credentails From The Same IP - Rule] type = detection -asset_type = +asset_type = Infrastructure 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - New container uploaded to AWS ECR - Rule] type = detection -asset_type = +asset_type = AWS ECR container confidence = medium 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 = {} +annotations = {"mitre_attack": ["T1525"]} known_false_positives = Uploading container is a normal behavior from developers or users with access to container registry. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - No Windows Updates in a time frame - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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 = none +providing_technologies = [] [savedsearch://ESCU - Okta Account Lockout Events - Rule] type = detection -asset_type = +asset_type = Infrastructure 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Okta Failed SSO Attempts - Rule] type = detection -asset_type = +asset_type = Infrastructure 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Okta User Logins From Multiple Cities - Rule] type = detection -asset_type = +asset_type = Infrastructure 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Open Redirect in Splunk Web - Rule] type = detection -asset_type = +asset_type = Splunk Server confidence = medium 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"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]} known_false_positives = None identified -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Osquery pack - ColdRoot detection - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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"], "nist": ["DE.DP", "DE.CM", "PR.PT"]} known_false_positives = There are no known false positives. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Overwriting Accessibility Binaries - Rule] type = detection -asset_type = +asset_type = Endpoint 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Process Execution via WMI - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Processes Tapping Keyboard Events - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Processes created by netsh - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Processes launching netsh - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Prohibited Network Traffic Allowed - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["T1043", "T1048"], "nist": ["DE.AE", "PR.AC"]} known_false_positives = None identified -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Prohibited Software On Endpoint - Rule] type = detection -asset_type = +asset_type = Endpoint 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"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = None identified -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Protocol or Port Mismatch - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["T1043"], "nist": ["DE.AE", "PR.AC"]} known_false_positives = None identified -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Protocols passing authentication in cleartext - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule] type = detection -asset_type = +asset_type = Endpoint 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": ["T1050", "T1031", "T1089"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]} +how_to_implement = none +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1058"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Reg exe used to hide files directories via registry keys - Rule] type = detection @@ -2286,121 +2502,121 @@ explanation = The search looks for command-line arguments used to hide a file or 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"], "nist": ["DE.CM"]} known_false_positives = None at the moment -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Registry Keys Used For Persistence - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Registry Keys Used For Privilege Escalation - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Registry Keys for Creating SHIM Databases - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Remote Desktop Network Bruteforce - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Remote Desktop Network Traffic - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} known_false_positives = Remote Desktop may be used legitimately by users on the network. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Remote Desktop Process Running On System - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} known_false_positives = Remote Desktop may be used legitimately by users on the network. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Remote Process Instantiation via WMI - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Remote Registry Key modifications - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Remote WMI Command Attempt - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - RunDLL Loading DLL By Ordinal - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - SMB Traffic Spike - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["T1043"], "nist": ["DE.CM"]} known_false_positives = A file server may experience high-demand loads that could cause this analytic to trigger. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - SMB Traffic Spike - MLTK - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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.\ @@ -2409,181 +2625,181 @@ This search produces a field (Number of events,count) that are not yet supported 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - SQL Injection with Long URLs - Rule] type = detection -asset_type = +asset_type = Database Server confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Samsam Test File Write - Rule] type = detection -asset_type = +asset_type = Endpoint 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"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = No false positives have been identified. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Sc exe Manipulating Windows Services - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["T1050", "T1031", "T1089"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]} +how_to_implement = none +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1050"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["T1053"], "nist": ["PR.IP"]} known_false_positives = No known false positives -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Scheduled tasks used in BadRabbit ransomware - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["T1053"], "nist": ["PR.IP"]} known_false_positives = No known false positives -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Schtasks scheduling job on remote system - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Schtasks used for forcing a reboot - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["T1053"], "nist": ["PR.IP"]} known_false_positives = Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Script Execution via WMI - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Shim Database File Creation - Rule] type = detection -asset_type = +asset_type = 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Shim Database Installation With Suspicious Parameters - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["T1138"], "nist": ["DE.CM"]} known_false_positives = None identified -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Short Lived Windows Accounts - Rule] type = detection -asset_type = +asset_type = Windows confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Single Letter Process On Endpoint - Rule] type = detection -asset_type = +asset_type = Endpoint 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Spectre and Meltdown Vulnerable Systems - Rule] type = detection -asset_type = +asset_type = Endpoint 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 = none +providing_technologies = [] [savedsearch://ESCU - Spike in File Writes - Rule] type = detection -asset_type = +asset_type = Endpoint 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Splunk Enterprise Information Disclosure - Rule] type = detection -asset_type = +asset_type = Splunk Server confidence = medium 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Suspicious Changes to File Associations - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Suspicious Email - UBA Anomaly - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Suspicious Email Attachment Extensions - Rule] type = detection -asset_type = +asset_type = Endpoint 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. \ @@ -2591,47 +2807,47 @@ how_to_implement = You need to ingest data from emails. Specifically, the sender 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"], "nist": ["DE.AE", "PR.IP"]} known_false_positives = None identified -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Suspicious File Write - Rule] type = detection -asset_type = +asset_type = Endpoint 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Suspicious Java Classes - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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"], "nist": ["DE.AE"]} known_false_positives = There are no known false positives. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Suspicious LNK file launching a process - Rule] type = detection -asset_type = +asset_type = Endpoint 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Suspicious Reg exe Process - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Suspicious wevtutil Usage - Rule] type = detection @@ -2641,67 +2857,67 @@ explanation = The wevtutil.exe application is the windows event log utility. Thi 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Suspicious writes to System Volume Information - Rule] type = detection -asset_type = +asset_type = Windows confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Suspicious writes to windows Recycle Bin - Rule] type = detection -asset_type = +asset_type = Windows confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - System Processes Run From Unexpected Locations - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["T1036"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = None identified -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - TOR Traffic - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["T1043"], "nist": ["DE.AE"]} known_false_positives = None at this time -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - USN Journal Deletion - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["T1070"], "nist": ["DE.CM", "PR.PT", "DE.AE", "DE.DP", "PR.IP"]} known_false_positives = None identified -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Uncommon Processes On Endpoint - Rule] type = detection -asset_type = +asset_type = Endpoint 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": ["T1015"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = None identified -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Unload Sysmon Filter Driver - Rule] type = detection @@ -2711,27 +2927,27 @@ explanation = Attackers often disable security tools to avoid detection. This se 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": ["T1089"], "nist": ["DE.CM"]} known_false_positives = -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Unsigned Image Loaded by LSASS - Rule] type = detection -asset_type = +asset_type = Windows confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Unsuccessful Netbackup backups - Rule] type = detection -asset_type = +asset_type = Endpoint 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 = none +providing_technologies = [] [savedsearch://ESCU - Unusually Long Command Line - Rule] type = detection @@ -2741,7 +2957,7 @@ explanation = Command lines that are extremely long may be indicative of malicio 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"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Some legitimate applications start with long command lines. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Unusually Long Command Line - MLTK - Rule] type = detection @@ -2751,112 +2967,120 @@ explanation = Command lines that are extremely long may be indicative of malicio 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - Unusually Long Content-Type Length - Rule] type = detection -asset_type = +asset_type = Web Server 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"], "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 = none +providing_technologies = [] [savedsearch://ESCU - WMI Permanent Event Subscription - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - WMI Permanent Event Subscription - Sysmon - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - WMI Temporary Event Subscription - Rule] type = detection -asset_type = +asset_type = Endpoint confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Web Fraud - Account Harvesting - Rule] type = detection -asset_type = +asset_type = Account confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Web Fraud - Anomalous User Clickspeed - Rule] type = detection -asset_type = +asset_type = account confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Web Fraud - Password Sharing Across Accounts - Rule] type = detection -asset_type = +asset_type = account confidence = medium 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 = none +providing_technologies = [] [savedsearch://ESCU - Web Servers Executing Suspicious Processes - Rule] type = detection -asset_type = +asset_type = Web Server confidence = medium 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Windows Event Log Cleared - Rule] type = detection -asset_type = +asset_type = Endpoint 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": ["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 = none +providing_technologies = [] [savedsearch://ESCU - Windows hosts file modification - Rule] type = detection -asset_type = +asset_type = Endpoint 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"], "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 = none +providing_technologies = [] ### END DETECTIONS ### ### RESPONSE TASKS ### +[savedsearch://ESCU - AWS Investigate Security Hub alerts by dest] +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 = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 + [savedsearch://ESCU - AWS Investigate User Activities By ARN] type = investigation explanation = none @@ -3650,10 +3874,31 @@ how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or lat known_false_positives = not defined providing_technologies = none -[savedsearch://ESCU - Previously Seen Running Windows Services] +[savedsearch://ESCU - Previously Seen Running Windows Services - Initial] type = support 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. +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 8.0.0 or above. +known_false_positives = not defined +providing_technologies = none + +[savedsearch://ESCU - Previously Seen Running Windows Services - Update] +type = support +explanation = This search returns the first and last time a Windows service was seen across your enterprise within the last hour. It then updates this information with historical data and filters out Windows services pairs that have not been seen within the specified time window. This updated table is then cached. +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 8.0.0 or above. +known_false_positives = not defined +providing_technologies = none + +[savedsearch://ESCU - Previously Seen Zoom Child Processes - Initial] +type = support +explanation = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS). This table is then cached. +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. +known_false_positives = not defined +providing_technologies = none + +[savedsearch://ESCU - Previously Seen Zoom Child Processes - Update] +type = support +explanation = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS) within the last hour. It then updates this information with historical data and filters out proces_name and endpoint pairs that have not been seen within the specified time window. This updated table is outputed to disk. +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. known_false_positives = not defined providing_technologies = none @@ -3685,6 +3930,13 @@ how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or lat known_false_positives = not defined providing_technologies = none +[savedsearch://ESCU - Previously seen users in CloudTrail - DM] +type = support +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 username, within the last 30 days. +how_to_implement = You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Validate the user name entries in `previously_seen_users_console_logins.csv`, which is a lookup file created by this support search. +known_false_positives = not defined +providing_technologies = none + [savedsearch://ESCU - Systems Ready for Spectre-Meltdown Windows Patch] type = support 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. @@ -3699,6 +3951,13 @@ how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or lat known_false_positives = not defined providing_technologies = none +[savedsearch://ESCU - Update previously seen users in CloudTrail - DM] +type = support +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 user, within the last hour. +how_to_implement = You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Validate the user name entries in `previously_seen_users_console_logins.csv`, which is a lookup file created by this support search. +known_false_positives = not defined +providing_technologies = none + [savedsearch://ESCU - Windows Updates Install Failures] type = support 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. diff --git a/package/default/workflow_actions.conf b/package/default/workflow_actions.conf index e18c065b73..10b27179de 100644 --- a/package/default/workflow_actions.conf +++ b/package/default/workflow_actions.conf @@ -1,4 +1,13 @@ +[workbench_panel_aws_investigate_security_hub_alerts_by_dest]] +label = Workbench - AWS Investigate Security Hub alerts by dest +type = link +fields = *dest* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_aws_investigate_security_hub_alerts_by_dest&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + [workbench_panel_aws_investigate_user_activities_by_arn]] label = Workbench - AWS Investigate User Activities By ARN type = link diff --git a/package/lookups/mitre_enrichment.csv b/package/lookups/mitre_enrichment.csv new file mode 100644 index 0000000000..fecfaed157 --- /dev/null +++ b/package/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 +T1221,Template Injection,Defense Evasion,APT28|Tropic Trooper|Dragonfly 2.0|DarkHydrus +T1220,XSL Script Processing,Defense Evasion|Execution,Cobalt Group +T1197,BITS Jobs,Defense Evasion|Persistence,Leviathan +T1217,Browser Bookmark Discovery,Discovery,no +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 +T1211,Exploitation for Defense Evasion,Defense Evasion,APT28 +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 +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|Elderwood|TA459 +T1208,Kerberoasting,Credential Access,no +T1215,Kernel Modules and Extensions,Persistence,no +T1202,Indirect Command Execution,Defense Evasion,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 +T1194,Spearphishing via Service,Initial Access,FIN6|OilRig|Dark Caracal|Magic Hound +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|APT19|OilRig|BRONZE BUTLER|FIN7|Dragonfly 2.0|APT32|FIN8|MuddyWater|APT28|TA459|Elderwood|APT29|menuPass|APT37|Patchwork|Leviathan|Magic Hound|PLATINUM +T1195,Supply Chain Compromise,Initial Access,APT41|Elderwood +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|BRONZE BUTLER|APT19|Dark Caracal|Cobalt Group|FIN7|Turla|OilRig|DarkHydrus|MuddyWater|Gorgon Group|Patchwork|Rancor|Lazarus Group|APT32|APT37|APT28|APT29|FIN8|menuPass|Leviathan|Elderwood|TA459|Magic Hound|PLATINUM +T1192,Spearphishing Link,Initial Access,Machete|Kimsuky|TA505|Stolen Pencil|APT39|FIN4|APT32|Night Dragon|APT28|Turla|Cobalt Group|OilRig|Dragonfly 2.0|APT33|Elderwood|APT29|Leviathan|Magic Hound|FIN8|Patchwork +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 +T1172,Domain Fronting,Command And Control,APT29 +T1173,Dynamic Data Exchange,Execution,TA505|MuddyWater|Gallmaker|Patchwork|Cobalt Group|APT37|APT28|FIN7 +T1181,Extra Window Memory Injection,Defense Evasion|Privilege Escalation,no +T1179,Hooking,Persistence|Privilege Escalation|Credential Access,PLATINUM +T1187,Forced Authentication,Credential Access,DarkHydrus|Dragonfly 2.0 +T1177,LSASS Driver,Execution|Persistence,no +T1171,LLMNR/NBT-NS Poisoning and Relay,Credential Access,no +T1188,Multi-hop Proxy,Command And Control,FIN4|APT29 +T1170,Mshta,Defense Evasion|Execution,Kimsuky|APT32|MuddyWater|FIN7 +T1183,Image File Execution Options Injection,Privilege Escalation|Persistence|Defense Evasion,TEMP.Veles +T1185,Man in the Browser,Collection,no +T1174,Password Filter DLL,Credential Access,no +T1184,SSH Hijacking,Lateral Movement,no +T1180,Screensaver,Persistence,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 +T1146,Clear Command History,Defense Evasion,APT41 +T1140,Deobfuscate/Decode Files or Information,Defense Evasion,Turla|WIRTE|Darkhotel|Tropic Trooper|Honeybee|menuPass|Gorgon Group|Threat Group-3390|APT19|Leviathan|MuddyWater|APT28|OilRig|BRONZE BUTLER +T1157,Dylib Hijacking,Persistence|Privilege Escalation,no +T1143,Hidden Window,Defense Evasion,Gorgon Group|Deep Panda|DarkHydrus|CopyKittens|APT19|APT32|APT28|APT3|Magic Hound +T1139,Bash History,Credential Access,no +T1148,HISTCONTROL,Defense Evasion,no +T1147,Hidden Users,Defense Evasion,no +T1136,Create Account,Persistence,APT41|Soft Cell|Dragonfly 2.0|Leafminer|APT3 +T1141,Input Prompt,Credential Access,FIN4 +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 +T1162,Login Item,Persistence,no +T1168,Local Job Scheduling,Persistence|Execution,no +T1137,Office Application Startup,Persistence,APT32|APT28 +T1142,Keychain,Credential Access,no +T1159,Launch Agent,Persistence,no +T1135,Network Share Discovery,Discovery,APT41|Tropic Trooper|APT1|Dragonfly 2.0|Sowbug +T1161,LC_LOAD_DYLIB Addition,Persistence,no +T1160,Launch Daemon,Persistence|Privilege Escalation,no +T1150,Plist Modification,Defense Evasion|Persistence|Privilege Escalation,no +T1145,Private Keys,Credential Access,no +T1163,Rc.common,Persistence,no +T1151,Space after Filename,Defense Evasion|Execution,no +T1153,Source,Execution,no +T1164,Re-opened Applications,Persistence,no +T1169,Sudo,Privilege Escalation,no +T1154,Trap,Execution|Persistence,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|APT28|Dragonfly 2.0|Magic Hound|Ke3chang|Leafminer|APT1 +T1113,Screen Capture,Collection,Silence|MuddyWater|OilRig|Dragonfly 2.0|FIN7|Dark Caracal|BRONZE BUTLER|Magic Hound|Group5|APT28 +T1112,Modify Registry,Defense Evasion,APT41|Turla|APT32|APT38|Dragonfly 2.0|Threat Group-3390|APT19|Patchwork|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|Cobalt Group|menuPass|Dragonfly 2.0|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|Rancor|Cobalt Group|Gorgon Group|Turla|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|Cobalt Group|Threat Group-3390|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|Cobalt Group|APT19|Gorgon Group|Thrip|APT28|Dragonfly 2.0|Leviathan|MuddyWater|TA459|FIN8|CopyKittens|BRONZE BUTLER|OilRig|Magic Hound|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|BRONZE BUTLER|Sowbug|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|OilRig|Magic Hound|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|APT32|FIN10|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|FIN10|menuPass|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|APT37|Ke3chang|Turla|Rancor|Honeybee|Orangeworm|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|Ke3chang|Dragonfly 2.0|Patchwork|Leafminer|Rancor|FIN7|Honeybee|Cobalt Group|APT37|Gorgon Group|MuddyWater|Leviathan|FIN8|TA459|APT28|Magic Hound|OilRig|BRONZE BUTLER|FIN5|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|Dragonfly 2.0|Gorgon Group|Cobalt Group|Honeybee|Threat Group-3390|Dark Caracal|Ke3chang|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|Threat Group-3390|menuPass|Patchwork|Suckfly|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|Cobalt Group|APT37|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|Patchwork|OilRig|Rancor|FIN8|BRONZE BUTLER|menuPass|FIN10|FIN7|APT32|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|Threat Group-3390|APT32|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|Cobalt Group|Leafminer|APT37|Threat Group-3390|Honeybee|Dark Caracal|APT19|FIN8|BlackOasis|Leviathan|Elderwood|MuddyWater|FIN7|APT3|Magic Hound|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|FIN5|APT3|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|APT28|Magic Hound|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/package/lookups/prohibited_apps_launching_cmd.csv b/package/lookups/prohibited_apps_launching_cmd.csv index 01edda483b..5e4a2cf651 100644 --- a/package/lookups/prohibited_apps_launching_cmd.csv +++ b/package/lookups/prohibited_apps_launching_cmd.csv @@ -14,3 +14,4 @@ firefox.exe,prohibited java.exe,prohibited powershell.exe,prohibited mshta.exe, prohibited +zoom.exe,prohibitied diff --git a/requirements.txt b/requirements.txt index 699430740c..ca85c08515 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,37 +1,42 @@ antlr4-python3-runtime==4.8 +appdirs==1.4.4 aspy.yaml==1.3.0 attackcti==0.3.2 attrs==19.3.0 -certifi==2020.4.5.1 -cfgv==2.0.1 +certifi==2020.6.20 +cfgv==3.1.0 chardet==3.0.4 -configparser==4.0.2 +configparser==5.0.0 contextlib2==0.6.0.post1 -identify==1.4.8 -idna==2.9 -importlib-metadata==1.2.0 -importlib-resources==1.0.2 -Jinja2==2.10.3 +distlib==0.3.1 +filelock==3.0.12 +gitdb==4.0.5 +identify==1.4.23 +idna==2.10 +importlib-metadata==1.7.0 +importlib-resources==3.0.0 +Jinja2==2.11.2 jsonschema==3.2.0 MarkupSafe==1.1.1 -more-itertools==8.0.2 -nodeenv==1.3.3 +more-itertools==8.4.0 +nodeenv==1.4.0 pathlib2==2.3.5 -pre-commit==1.20.0 -pyrsistent==0.15.6 +pre-commit==2.6.0 +pyrsistent==0.16.0 python-dateutil==2.8.1 -pytz==2019.3 +pytz==2020.1 PyYAML==5.3.1 -requests==2.23.0 +requests==2.24.0 scandir==1.10.0 -semantic-version==2.8.3 +semantic-version==2.8.5 simplejson==3.17.0 -six==1.14.0 -stix2==1.4.0 -stix2-patterns==1.3.0 -taxii2-client==2.0.0 -toml==0.10.0 +six==1.15.0 +smmap==3.0.4 +stix2==2.0.2 +stix2-patterns==1.3.1 +taxii2-client==2.2.0 +toml==0.10.1 typing==3.7.4.1 urllib3==1.25.9 -virtualenv==16.7.8 -zipp==0.6.0 +virtualenv==20.0.26 +zipp==3.1.0 diff --git a/response_tasks/aws_investigate_security_hub_alerts_by_dest.yml b/response_tasks/aws_investigate_security_hub_alerts_by_dest.yml new file mode 100644 index 0000000000..9d4425c5dc --- /dev/null +++ b/response_tasks/aws_investigate_security_hub_alerts_by_dest.yml @@ -0,0 +1,19 @@ +name: AWS Investigate Security Hub alerts by dest +id: b0d2e6a8-75fa-4b1b-9486-3d32acadf822 +version: 1 +date: '2020-06-08' +description: This search retrieves the all the alerts created by AWS Security Hub for a specific dest(instance_id). +how_to_implement: You must 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: +- dest +search: 'sourcetype="aws:securityhub:firehose" "findings{}.Resources{}.Type"=AWSEC2Instance +| rex field=findings{}.Resources{}.Id .*instance/(?.*) | search instance = $dest$ |rename findings{}.* as * | rename Remediation.Recommendation.Text as Remediation | table instance Title ProductArn Description FirstObservedAt RecordState Remediation' +tags: + analytics_story: + - Cloud Compute Instance + - Cloud Cryptomining + - Suspicious AWS EC2 Activities + - AWS Suspicious Provisioning Activities diff --git a/response_tasks/aws_investigate_user_activities_by_arn.yml b/response_tasks/aws_investigate_user_activities_by_arn.yml index a6550aedde..e7d26efce7 100644 --- a/response_tasks/aws_investigate_user_activities_by_arn.yml +++ b/response_tasks/aws_investigate_user_activities_by_arn.yml @@ -1,5 +1,5 @@ name: AWS Investigate User Activities By ARN -id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 +id: bc91a8cd-35e7-4bb2-6140-e756cc46fd72 version: 2 date: '2019-04-30' description: This search lists all the logged CloudTrail activities by a specific diff --git a/response_tasks/get_authentication_logs_for_endpoint.yml b/response_tasks/get_authentication_logs_for_endpoint.yml index 7c8c273710..1539cbd67e 100644 --- a/response_tasks/get_authentication_logs_for_endpoint.yml +++ b/response_tasks/get_authentication_logs_for_endpoint.yml @@ -54,3 +54,4 @@ tags: - Windows Persistence Techniques - Windows Privilege Escalation - Windows Service Abuse + - Suspicious Zoom Child Processes diff --git a/response_tasks/get_notable_history.yml b/response_tasks/get_notable_history.yml index 6a7a5bb371..418a5c5ca1 100644 --- a/response_tasks/get_notable_history.yml +++ b/response_tasks/get_notable_history.yml @@ -55,6 +55,7 @@ tags: - Suspicious AWS EC2 Activities - Suspicious AWS S3 Activities - Suspicious AWS Traffic + - Suspicious Cloud Authentication Activities - Suspicious Command-Line Executions - Suspicious DNS Traffic - Suspicious Emails diff --git a/response_tasks/get_notable_info.yml b/response_tasks/get_notable_info.yml index b51d7d48b7..90491c4aac 100644 --- a/response_tasks/get_notable_info.yml +++ b/response_tasks/get_notable_info.yml @@ -67,3 +67,5 @@ tags: - Windows Persistence Techniques - Windows Privilege Escalation - Windows Service Abuse + - Kubernetes Sensitive Role Activity + - Kubernetes Sensitive Object Access Activity diff --git a/response_tasks/get_process_file_activity.yml b/response_tasks/get_process_file_activity.yml index 541db0c677..8442bbdfd1 100644 --- a/response_tasks/get_process_file_activity.yml +++ b/response_tasks/get_process_file_activity.yml @@ -18,3 +18,4 @@ search: '| tstats `security_content_summariesonly` values(Filesystem.file_name) tags: analytics_story: - DHS Report TA18-074A + - Suspicious Zoom Child Processes diff --git a/response_tasks/get_process_registry_activity.yml b/response_tasks/get_process_registry_activity.yml index 71b74cb8ad..c522b90708 100644 --- a/response_tasks/get_process_registry_activity.yml +++ b/response_tasks/get_process_registry_activity.yml @@ -18,3 +18,4 @@ search: '| tstats `security_content_summariesonly` values(Registry.registry_key_ tags: analytics_story: - DHS Report TA18-074A + - Suspicious Zoom Child Processes diff --git a/response_tasks/investigate_aws_user_activities_by_user_field.yml b/response_tasks/investigate_aws_user_activities_by_user_field.yml index b2fcc388ae..cc9aebbd48 100644 --- a/response_tasks/investigate_aws_user_activities_by_user_field.yml +++ b/response_tasks/investigate_aws_user_activities_by_user_field.yml @@ -17,3 +17,4 @@ search: '| search sourcetype=aws:cloudtrail user=$user$ | table _time userIdenti tags: analytics_story: - AWS User Monitoring + - Suspicious Cloud Authentication Activities diff --git a/response_tasks/investigate_pass_the_ticket_attempts.yml b/response_tasks/investigate_pass_the_ticket_attempts.yml index e6de16dd57..7ca5f470c9 100644 --- a/response_tasks/investigate_pass_the_ticket_attempts.yml +++ b/response_tasks/investigate_pass_the_ticket_attempts.yml @@ -12,8 +12,10 @@ how_to_implement: To successfully implement this search you need to be ingesting author: Patrick Bareiss, Splunk inputs: - dest -search: '`wineventlog_security` EventCode=4768 OR EventCode=4769 | stats count BY user, dest, EventCode | stats max(count) - AS max_count sum(count) AS sum_count BY user, dest | where sum_count/max_count!=2' +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/spec/deployments.spec.json b/spec/deployments.spec.json index c417cc0bf3..7014cfd201 100644 --- a/spec/deployments.spec.json +++ b/spec/deployments.spec.json @@ -256,7 +256,6 @@ "date", "description", "scheduling", - "alert_action", "tags" ], "title": "Deployment Schema", diff --git a/spec/lookups.spec.json b/spec/lookups.spec.json index 0c85720259..4d59673a35 100644 --- a/spec/lookups.spec.json +++ b/spec/lookups.spec.json @@ -47,6 +47,13 @@ ], "type": "string" }, + "fields_list": { + "description": "A comma and space separated list of field names", + "examples": [ + "_key, dest, process_name" + ], + "type": "string" + }, "filename": { "description": "The name of the file to use for this lookup", "examples": [ @@ -54,6 +61,13 @@ ], "type": "string" }, + "filter": { + "description": "Use this attribute to improve search performance when working with significantly large KV", + "examples": [ + "dest=\"SPLK_*\"" + ], + "type": "string" + }, "match_type": { "description": "A comma and space-delimited list of () specification to allow for non-exact matching", "examples": [ diff --git a/stories/account_monitoring_and_controls.yml b/stories/account_monitoring_and_controls.yml index f03fe46e46..97aa2c5e63 100644 --- a/stories/account_monitoring_and_controls.yml +++ b/stories/account_monitoring_and_controls.yml @@ -15,8 +15,7 @@ narrative: Monitoring user accounts within your enterprise is a critical analyti or co-opted, leading to suspicious or malicious activity. author: David Dorsey, Splunk type: ESCU -references: -- https://www.sans.org/media/critical-security-controls/critical-controls-poster-2016.pdf +references: [] tags: analytics_story: Account Monitoring and Controls usecase: Security Monitoring diff --git a/stories/kubernetes_sensitive_role_activity.yml b/stories/kubernetes_sensitive_role_activity.yml index 7d52e04e08..739bce3ae7 100644 --- a/stories/kubernetes_sensitive_role_activity.yml +++ b/stories/kubernetes_sensitive_role_activity.yml @@ -1,13 +1,13 @@ author: "Rod Soto, Splunk" date: "2020-05-20" description: "This story addresses detection and response around Sensitive Role usage within a Kubernetes clusters against cluster resources and namespaces." -id: 2574e6d9-7254-4751-8925-0447deeec8ea -name: "Kubernetes Sensitive Object Access Activity" -narrative: "Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive objects within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes sensitive objects." +id: 2574e6d9-7254-4751-8925-0447deeec8ew +name: "Kubernetes Sensitive Role Activity" +narrative: "Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive roles within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes role activities" references: - https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html tags: - analytics_story: "Kubernetes Sensitive Object Access Activity" + analytics_story: "Kubernetes Sensitive Role Activity" category: - "Cloud Security" usecase: "Security Monitoring" diff --git a/stories/netsh_abuse.yml b/stories/netsh_abuse.yml index f294b5483c..584a2b50e9 100644 --- a/stories/netsh_abuse.yml +++ b/stories/netsh_abuse.yml @@ -17,7 +17,7 @@ narrative: 'It is a common practice for attackers of all types to leverage nativ author: Bhavin Patel, Splunk type: ESCU references: -- https://technet.microsoft.com/library/bb490939.aspx +- https://docs.microsoft.com/en-us/previous-versions/tn-archive/bb490939(v=technet.10) - 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: diff --git a/stories/suspicious_cloud_authentication_activities.yml b/stories/suspicious_cloud_authentication_activities.yml new file mode 100644 index 0000000000..09465bd04a --- /dev/null +++ b/stories/suspicious_cloud_authentication_activities.yml @@ -0,0 +1,27 @@ + +name: Suspicious Cloud Authentication Activities +id: 6380ebbb-55c5-4fce-b754-01fd565fb73c +version: 1 +date: '2020-06-04' +description: 'Monitor your cloud authentication events. Searches within this Analytic Story leverage + the recent cloud updates to the Authentication data model to help you stay aware of and investigate + suspicious login activity. ' +narrative: 'It is important to monitor and control who has access to your cloud infrastructure. + Detecting suspicious logins 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 + compute activity whether legitimate or otherwise.\ + + This Analytic Story has data model versions of cloud searches leveraging Authentication data, + including those looking for suspicious login activity, and cross-account activity for AWS.' +author: Rico Valdez, Splunk +type: ESCU +references: +- https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/ +- https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html +tags: + analytics_story: Suspicious Cloud Authentication Activities + usecase: Security Monitoring + category: + - Cloud Security + + diff --git a/stories/suspicious_zoom_child_processes.yml b/stories/suspicious_zoom_child_processes.yml new file mode 100644 index 0000000000..72d7e859c4 --- /dev/null +++ b/stories/suspicious_zoom_child_processes.yml @@ -0,0 +1,24 @@ +name: Suspicious Zoom Child Processes +id: aa3749a6-49c7-491e-a03f-4eaee5fe0258 +version: 1 +date: '2020-04-13' +description: Attackers are using Zoom as an vector to increase privileges on a sytems. This story detects new + child processes of zoom and provides investigative actions for this detection. +narrative: 'Zoom is a leader in modern enterprise video communications and its usage has + increased dramatically with a large amount of the population under stay-at-home orders + due to the COVID-19 pandemic. With increased usage has come increased scrutiny and + several security flaws have been found with this application on both Windows and macOS + systems.\ + + Current detections focus on finding new child processes of this application on a per host + basis. Investigative searches are included to gather information needed during an investigation.' +author: David Dorsey, Splunk +type: ESCU +references: + - https://blog.rapid7.com/2020/04/02/dispelling-zoom-bugbears-what-you-need-to-know-about-the-latest-zoom-vulnerabilities/ + - https://threatpost.com/two-zoom-zero-day-flaws-uncovered/154337/ +tags: + analytics_story: Suspicious Zoom Child Processes + usecase: Advanced Threat Detection + category: + - Adversary Tactics diff --git a/tests/T1003_001.yml b/tests/T1003_001.yml new file mode 100644 index 0000000000..00f8799dfe --- /dev/null +++ b/tests/T1003_001.yml @@ -0,0 +1,15 @@ +name: Credential Dumping LSASS memory test +detections: + - name: Access LSASS Memory for Dump Creation + pass_condition: '| stats count | where count = 2' + - name: Create Remote Thread into LSASS + pass_condition: '| stats count | where count = 1' + - name: Detect Credential Dumping through LSASS access + pass_condition: '| stats count | where count = 1' + - name: Dump LSASS via comsvcs DLL + pass_condition: '| stats count | where count = 1' + - name: Unsigned Image Loaded by LSASS + pass_condition: '| stats count | where count > 0' +description: Test credential dumping detections +target: attack-range-windows-domain-controller +simulation_technique: 'T1003.001' diff --git a/tests/T1003_002.yml b/tests/T1003_002.yml new file mode 100644 index 0000000000..487c48a3d5 --- /dev/null +++ b/tests/T1003_002.yml @@ -0,0 +1,7 @@ +name: Credential Dumping sam test +detections: + - name: Attempted Credential Dump From Registry via Reg exe + pass_condition: '| stats count | where count = 4' +description: Test credential dumping detections +target: attack-range-windows-domain-controller +simulation_technique: 'T1003.002' diff --git a/tests/T1003_003.yml b/tests/T1003_003.yml new file mode 100644 index 0000000000..f857e25580 --- /dev/null +++ b/tests/T1003_003.yml @@ -0,0 +1,13 @@ +name: Credential Dumping Ntds test +detections: + - name: Creation of Shadow Copy + pass_condition: '| stats count | where count = 4' + - name: Creation of Shadow Copy with wmic and powershell + pass_condition: '| stats count | where count = 2' + - name: Credential Dumping via Copy Command from Shadow Copy + pass_condition: '| stats count | where count = 1' + - name: Credential Dumping via Symlink to Shadow Copy + pass_condition: '| stats count | where count = 1' +description: Test credential dumping detections +target: attack-range-windows-domain-controller +simulation_technique: 'T1003.003' diff --git a/tests/T1021_001.yml b/tests/T1021_001.yml new file mode 100644 index 0000000000..1a0c7e4cf7 --- /dev/null +++ b/tests/T1021_001.yml @@ -0,0 +1,7 @@ +name: Remote Desktop Connection +detections: + - name: Remote Desktop Network Traffic + pass_condition: '| stats count | where count > 0' +description: Test remote desktop connections +target: attack-range-windows-domain-controller +simulation_technique: 'T1021.001' diff --git a/tests/T1136_001.yml b/tests/T1136_001.yml new file mode 100644 index 0000000000..257fba3c42 --- /dev/null +++ b/tests/T1136_001.yml @@ -0,0 +1,9 @@ +name: Create local account test +detections: + - name: Short Lived Windows Accounts + pass_condition: '| stats count | where count = 1' + - name: Detect New Local Admin account + pass_condition: '| stats count | where count = 1' +description: Test Create local account +target: attack-range-windows-domain-controller +simulation_technique: 'T1136.001' diff --git a/tests/T1551_001.yml b/tests/T1551_001.yml new file mode 100644 index 0000000000..7b00e06592 --- /dev/null +++ b/tests/T1551_001.yml @@ -0,0 +1,7 @@ +name: Clear Windows Event Logs +detections: + - name: Windows Event Log Cleared + pass_condition: '| stats count | where count = 4' +description: Test Windows Event Logs cleared +target: attack-range-windows-domain-controller +simulation_technique: 'T1551.001' diff --git a/tests/T1574_009.yml b/tests/T1574_009.yml new file mode 100644 index 0000000000..6a6023f8c3 --- /dev/null +++ b/tests/T1574_009.yml @@ -0,0 +1,7 @@ +name: Hijack Execution Flow Path Interception by Unquoted Path +detections: + - name: Detect Path Interception By Creation Of program exe + pass_condition: '| stats count | where count = 1' +description: Test attacks on unquoted service paths +target: attack-range-windows-domain-controller +simulation_technique: 'T1574.009'