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divious1
2019-09-04 12:20:27 -04:00
parent 621f26301b
commit fe0b0e1133
228 changed files with 16061 additions and 16061 deletions
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baselines/add_prohibited_processes_to_es.json
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@@ -1,46 +1,46 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| inputlookup interesting_processes_lookup | search note!=ESCU* | inputlookup append=T prohibitedProcesses_lookup | fillnull value=* dest dest_pci_domain | fillnull value=false is_required is_secure | fillnull value=true is_prohibited | outputlookup interesting_processes_lookup | stats count"
}
},
"creation_date": "2017-06-27",
"data_metadata": {
"data_models": [],
"data_source": [
"Splunk Enterprise"
],
"providing_technologies": [
"Splunk Enterprise Security"
]
},
"description": "This search takes the existing interesting process table from ES, filters out any existing additions added by ESCU and then updates the table with processes identified by ESCU that should be prohibited on your endpoints.",
"eli5": "This search outputs the interesting processes lookup table and filters out all processes in the table that haven't already been inserted by ESCU. It then appends to those results all the processes currently identified by ESCU that should be prohibited. Next, it fills in the required fields with processes identified by ESCU, and then writes the results back to the interesting process lookup table. This is done so any new processes identified that should be prohibited will be added to the lookup table without creating any duplicate entries.",
"how_to_implement": "This search should be run on each new install of ESCU.",
"id": "251930a5-1451-4428-bb13-eed5775be0ce",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| inputlookup interesting_processes_lookup | search note!=ESCU* | inputlookup append=T prohibitedProcesses_lookup | fillnull value=* dest dest_pci_domain | fillnull value=false is_required is_secure | fillnull value=true is_prohibited | outputlookup interesting_processes_lookup | stats count"
}
},
"creation_date": "2017-06-27",
"data_metadata": {
"data_models": [],
"data_source": [
"Splunk Enterprise"
],
"modification_date": "2017-09-15",
"name": "Add Prohibited Processes to Enterprise Security",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"Splunk Enterprise Security"
]
},
"description": "This search takes the existing interesting process table from ES, filters out any existing additions added by ESCU and then updates the table with processes identified by ESCU that should be prohibited on your endpoints.",
"eli5": "This search outputs the interesting processes lookup table and filters out all processes in the table that haven't already been inserted by ESCU. It then appends to those results all the processes currently identified by ESCU that should be prohibited. Next, it fills in the required fields with processes identified by ESCU, and then writes the results back to the interesting process lookup table. This is done so any new processes identified that should be prohibited will be added to the lookup table without creating any duplicate entries.",
"how_to_implement": "This search should be run on each new install of ESCU.",
"id": "251930a5-1451-4428-bb13-eed5775be0ce",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"modification_date": "2017-09-15",
"name": "Add Prohibited Processes to Enterprise Security",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/approved_aws_service_accounts.json
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@@ -1,48 +1,48 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail errorCode=success | rename userName as identity | search NOT [inputlookup identity_lookup_expanded | fields identity] | stats count by identity | table identity | outputlookup aws_service_accounts | stats count"
}
},
"creation_date": "2018-03-12",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for successful API activity in CloudTrail within the last 30 days, filters out known users from the identity table, and outputs values of users into <code>aws_service_accounts.csv</code> lookup file.",
"eli5": "We first look for all successful CloudTrail API activity caused by types of user accounts and then remove all the events caused by users in the Identity table. This generates a list of accounts--typically service accounts--configured in your AWS environment. We output this list of service accounts to <code>aws_service_accounts.csv</code>.",
"how_to_implement": "You must install the AWS App for 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 <code>aws_service_accounts.csv</code>, which is a lookup file created as a result of running this support search. Please remove the entries of service accounts that are not legitimate.",
"id": "fc0edc95-ff2b-48b1-5f6f-63ga3789fd43",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail errorCode=success | rename userName as identity | search NOT [inputlookup identity_lookup_expanded | fields identity] | stats count by identity | table identity | outputlookup aws_service_accounts | stats count"
}
},
"creation_date": "2018-03-12",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"modification_date": "2018-12-03",
"name": "Create a list of approved AWS service accounts",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
"data_sourcetypes": [
"aws:cloudtrail"
],
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for successful API activity in CloudTrail within the last 30 days, filters out known users from the identity table, and outputs values of users into `aws_service_accounts.csv` lookup file.",
"eli5": "We first look for all successful CloudTrail API activity caused by types of user accounts and then remove all the events caused by users in the Identity table. This generates a list of accounts--typically service accounts--configured in your AWS environment. We output this list of service accounts to `aws_service_accounts.csv`.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the service account entires in `aws_service_accounts.csv`, which is a lookup file created as a result of running this support search. Please remove the entries of service accounts that are not legitimate.",
"id": "fc0edc95-ff2b-48b1-5f6f-63ga3789fd43",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"modification_date": "2018-12-03",
"name": "Create a list of approved AWS service accounts",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
baselines/assets_sorted_by_category.json
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@@ -1,48 +1,48 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| from datamodel Identity_Management.All_Assets | stats count values(nt_host) by category | sort -count"
}
},
"creation_date": "2017-06-11",
"data_metadata": {
"data_models": [
"Identity_Management"
],
"data_source": [
"Splunk Enterprise Security"
],
"providing_technologies": [
"Splunk Enterprise Security"
]
},
"description": "This search shows you every asset category you have and the assets that belong to those categories.",
"eli5": "This search gives you the number and the names of the hosts of each host in your environment by category. It will then sort them by the count.",
"how_to_implement": "To successfully implement this search you must first leverage the Assets and Identity framework in Enterprise Security to populate your assets_by_str.csv file which should then be mapped to the Identity_Management data model. The Identity_Management data model will contain a list of known authorized company assets. Ensure that all inventoried systems are constantly vetted and updated.",
"id": "dcfd6b40-42f9-469d-a433-2e53f7489ff9",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| from datamodel Identity_Management.All_Assets | stats count values(nt_host) by category | sort -count"
}
},
"creation_date": "2017-06-11",
"data_metadata": {
"data_models": [
"Identity_Management"
],
"modification_date": "2017-09-13",
"name": "Count of assets by category",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
"data_source": [
"Splunk Enterprise Security"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"Splunk Enterprise Security"
]
},
"description": "This search shows you every asset category you have and the assets that belong to those categories.",
"eli5": "This search gives you the number and the names of the hosts of each host in your environment by category. It will then sort them by the count.",
"how_to_implement": "To successfully implement this search you must first leverage the Assets and Identity framework in Enterprise Security to populate your assets_by_str.csv file which should then be mapped to the Identity_Management data model. The Identity_Management data model will contain a list of known authorized company assets. Ensure that all inventoried systems are constantly vetted and updated.",
"id": "dcfd6b40-42f9-469d-a433-2e53f7489ff9",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"modification_date": "2017-09-13",
"name": "Count of assets by category",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/baseline_api_calls_per_user_arn.json
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@@ -1,48 +1,48 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-90d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventType=AwsApiCall | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | stats count"
}
},
"creation_date": "2018-04-09",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.",
"eli5": "This search returns all log events that are API calls, pulls out the ARN that initiated each call, and collects them in one-hour groupings. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points each ARN had. This table is then stored in a lookup file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.",
"id": "fc0edc96-ff2b-48b0-9f6f-63da3783fd63",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-90d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventType=AwsApiCall | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | stats count"
}
},
"creation_date": "2018-04-09",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"modification_date": "2018-04-09",
"name": "Baseline of API Calls per User ARN",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"data_sourcetypes": [
"aws:cloudtrail"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"AWS"
]
},
"description": "This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.",
"eli5": "This search returns all log events that are API calls, pulls out the ARN that initiated each call, and collects them in one-hour groupings. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points each ARN had. This table is then stored in a lookup file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.",
"id": "fc0edc96-ff2b-48b0-9f6f-63da3783fd63",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"modification_date": "2018-04-09",
"name": "Baseline of API Calls per User ARN",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/baseline_aws_regions.json
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@@ -1,48 +1,48 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv | stats count"
}
},
"creation_date": "2018-01-08",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days",
"eli5": "In this support search, we create a table of the first time (earliest) and most recent time (latest) that this region has been seen in our dataset, grouped by the value <code>awsRegion</code>. We only look for those events where an instance has been started. All of these entries will be added to the <code>previously_seen_aws_regions.csv</code> lookup file, which will act like a baseline for detections. Please validate the entries of region names in the lookup file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.",
"id": "fc0edc95-ff2b-48b0-9f6f-63da3789fd63",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv | stats count"
}
},
"creation_date": "2018-01-08",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"modification_date": "2018-01-08",
"name": "Previously Seen AWS Regions",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
"data_sourcetypes": [
"aws:cloudtrail"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days",
"eli5": "In this support search, we create a table of the first time (earliest) and most recent time (latest) that this region has been seen in our dataset, grouped by the value `awsRegion`. We only look for those events where an instance has been started. All of these entries will be added to the `previously_seen_aws_regions.csv` lookup file, which will act like a baseline for detections. Please validate the entries of region names in the lookup file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.",
"id": "fc0edc95-ff2b-48b0-9f6f-63da3789fd63",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"modification_date": "2018-01-08",
"name": "Previously Seen AWS Regions",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/baseline_blocked_vpc_traffic.json
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@@ -1,48 +1,48 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudwatchlogs:vpcflow action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | bucket _time span=1h | stats count as numberOfBlockedConnections by _time, src_ip | stats count(numberOfBlockedConnections) as numDataPoints, latest(numberOfBlockedConnections) as latestCount, avg(numberOfBlockedConnections) as avgBlockedConnections, stdev(numberOfBlockedConnections) as stdevBlockedConnections by src_ip | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | stats count"
}
},
"creation_date": "2018-04-26",
"data_metadata": {
"data_source": [
"AWS VPC Flow Logs"
],
"data_sourcetypes": [
"aws:cloudwatchlogs:vpcflow"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search establishes, on a per-hour basis, the average and the standard deviation of the number of outbound connections blocked in your VPC flow logs by each source IP address (IP address of your EC2 instances). Also recorded is the number of data points for each source IP. This table outputs to a lookup file to allow the detection search to operate quickly.",
"eli5": "Use this search to create a baseline of blocked outbound network connections by each source IP in your AWS environment. This search returns all log events that correspond to a blocked outbound network connection, extracts the source IP from where the outbound connection was initiated, and collects the events in one-hour groupings. Next, it calculates the number of outbound connections blocked per hour. For each source IP, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points each source IP had. This table is then stored in a lookup file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your <code>VPC flow logs.</code>.",
"id": "fc0edd96-ff2b-48b0-9f1f-63da3782fd63",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudwatchlogs:vpcflow action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | bucket _time span=1h | stats count as numberOfBlockedConnections by _time, src_ip | stats count(numberOfBlockedConnections) as numDataPoints, latest(numberOfBlockedConnections) as latestCount, avg(numberOfBlockedConnections) as avgBlockedConnections, stdev(numberOfBlockedConnections) as stdevBlockedConnections by src_ip | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | stats count"
}
},
"creation_date": "2018-04-26",
"data_metadata": {
"data_source": [
"AWS VPC Flow Logs"
],
"modification_date": "2018-05-07",
"name": "Baseline of blocked outbound traffic from AWS",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
"data_sourcetypes": [
"aws:cloudwatchlogs:vpcflow"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"AWS"
]
},
"description": "This search establishes, on a per-hour basis, the average and the standard deviation of the number of outbound connections blocked in your VPC flow logs by each source IP address (IP address of your EC2 instances). Also recorded is the number of data points for each source IP. This table outputs to a lookup file to allow the detection search to operate quickly.",
"eli5": "Use this search to create a baseline of blocked outbound network connections by each source IP in your AWS environment. This search returns all log events that correspond to a blocked outbound network connection, extracts the source IP from where the outbound connection was initiated, and collects the events in one-hour groupings. Next, it calculates the number of outbound connections blocked per hour. For each source IP, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points each source IP had. This table is then stored in a lookup file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your `VPC flow logs.`.",
"id": "fc0edd96-ff2b-48b0-9f1f-63da3782fd63",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"modification_date": "2018-05-07",
"name": "Baseline of blocked outbound traffic from AWS",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/baseline_network_acl_modifications.json
+45 -45
View File
@@ -1,48 +1,48 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail `NetworkACLEvents` | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup network_acl_activity_baseline | stats count"
}
},
"creation_date": "2018-05-21",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls that were related to network ACLs made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.",
"eli5": "Use this search to create a baseline for API calls related to network ACLs for the users who initiated this activity. It returns all logged API calls for network activity, pulls out the ARN that initiated each call, and collects the <code>eventNames</code> in one-hour groupings. Next, it calculates the number of API calls made per ARN per-hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in a lookup file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for network ACLs, edit the macro <code>NetworkACLEvents</code>.",
"id": "fc0edd96-ff2b-4810-9f1f-63da3783fd63",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail `NetworkACLEvents` | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup network_acl_activity_baseline | stats count"
}
},
"creation_date": "2018-05-21",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"modification_date": "2018-05-21",
"name": "Baseline of Network ACL Activity by ARN",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
"data_sourcetypes": [
"aws:cloudtrail"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"AWS"
]
},
"description": "This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls that were related to network ACLs made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.",
"eli5": "Use this search to create a baseline for API calls related to network ACLs for the users who initiated this activity. It returns all logged API calls for network activity, pulls out the ARN that initiated each call, and collects the `eventNames` in one-hour groupings. Next, it calculates the number of API calls made per ARN per-hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in a lookup file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for network ACLs, edit the macro `NetworkACLEvents`.",
"id": "fc0edd96-ff2b-4810-9f1f-63da3783fd63",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"modification_date": "2018-05-21",
"name": "Baseline of Network ACL Activity by ARN",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/baseline_s3_deletion_by_arn.json
+45 -45
View File
@@ -1,48 +1,48 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-90d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventName=DeleteBucket | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | stats count"
}
},
"creation_date": "2018-07-17",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search establishes, on a per-hour basis, the average and standard deviation for the number of API calls related to deleting an S3 bucket by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.",
"eli5": "Use this search to create a baseline for API calls related to deleting an S3 bucket, grouped by the users who initiated this activity. It returns all logged API calls for S3 bucket-deletion activity and then pulls out the ARN that initiated each call. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in a lookup file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.",
"id": "fc0edd96-ff2b-48b0-9f1f-63eq3783fd63",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-90d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventName=DeleteBucket | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | stats count"
}
},
"creation_date": "2018-07-17",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"modification_date": "2018-07-17",
"name": "Baseline of S3 Bucket deletion activity by ARN",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
"data_sourcetypes": [
"aws:cloudtrail"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"AWS"
]
},
"description": "This search establishes, on a per-hour basis, the average and standard deviation for the number of API calls related to deleting an S3 bucket by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.",
"eli5": "Use this search to create a baseline for API calls related to deleting an S3 bucket, grouped by the users who initiated this activity. It returns all logged API calls for S3 bucket-deletion activity and then pulls out the ARN that initiated each call. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in a lookup file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.",
"id": "fc0edd96-ff2b-48b0-9f1f-63eq3783fd63",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"modification_date": "2018-07-17",
"name": "Baseline of S3 Bucket deletion activity by ARN",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/baseline_security_group_activity_by_arn.json
+45 -45
View File
@@ -1,48 +1,48 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-90d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail `securityGroupAPIs` | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup security_group_activity_baseline | stats count"
}
},
"creation_date": "2018-04-17",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search establishes, on a per-hour basis, the average and the standard deviation for the number of API calls related to security groups made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.",
"eli5": "Use this search to create a baseline for API calls related to security groups by the users who initiated this activity. It returns all logged API calls for all security-group-related activity, pulls out the ARN that initiated each call, and collects the <code>eventNames</code> in one-hour groupings. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in a lookup file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for security groups, edit the macro <code>securityGroupAPIs</code>.",
"id": "fc0edd96-ff2b-48b0-9f1f-63da3783fd63",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-90d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail `securityGroupAPIs` | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup security_group_activity_baseline | stats count"
}
},
"creation_date": "2018-04-17",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"modification_date": "2018-04-17",
"name": "Baseline of Security Group Activity by ARN",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
"data_sourcetypes": [
"aws:cloudtrail"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"AWS"
]
},
"description": "This search establishes, on a per-hour basis, the average and the standard deviation for the number of API calls related to security groups made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.",
"eli5": "Use this search to create a baseline for API calls related to security groups by the users who initiated this activity. It returns all logged API calls for all security-group-related activity, pulls out the ARN that initiated each call, and collects the `eventNames` in one-hour groupings. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in a lookup file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for security groups, edit the macro `securityGroupAPIs`.",
"id": "fc0edd96-ff2b-48b0-9f1f-63da3783fd63",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"modification_date": "2018-04-17",
"name": "Baseline of Security Group Activity by ARN",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/brand_abuse_dnstwist_domains.json
+43 -43
View File
@@ -1,45 +1,45 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| dnstwist domainlist=domains.csv | `remove_valid_domains` | eval domain_abuse=\"true\" | table domain, domain_abuse | outputlookup brandMonitoring_lookup | stats count"
}
},
"creation_date": "2017-06-01",
"data_metadata": {
"data_source": [
"Splunk Enterprise Security Content Update"
],
"providing_technologies": [
"Splunk Enterprise"
]
},
"description": "This search creates permutations of your existing domains, removes the valid domain names and stores them in a specified lookup file so they can be checked for in the associated detection searches.",
"eli5": "This search starts with the dnstwist command consuming domains from a file called domains.csv in the DA-ESS-SOC/lookups directory. This search then adds a domain\\_abuse=true term to each permutation, removes all the valid domain names and stores all that information into a lookup file that is used in the associated detection search. Alternatively domain dnstwist permutations can be calculated from domains in the `cim_corporate_email_domains.csv` and `cim_corporate_web_domains.csv` lookups located in **Splunk\\_SA\\_CIM** using argument `populate_from_cim=true`. Also an individual domain can be passed using argument `domain=<domain>`",
"how_to_implement": "To successfully implement this search you need to update the file called domains.csv in the DA-ESS-SOC/lookup directory. Or `cim_corporate_email_domains.csv` and `cim_corporate_web_domains.csv` from **Splunk\\_SA\\_CIM**.",
"id": "19f7d2ec-6028-4d01-bcdb-bda9a034c17f",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "jhernandez@splunk.com",
"name": "Jose Hernandez"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| dnstwist domainlist=domains.csv | `remove_valid_domains` | eval domain_abuse=\"true\" | table domain, domain_abuse | outputlookup brandMonitoring_lookup | stats count"
}
},
"creation_date": "2017-06-01",
"data_metadata": {
"data_source": [
"Splunk Enterprise Security Content Update"
],
"modification_date": "2018-10-08",
"name": "DNSTwist Domain Names",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
"providing_technologies": [
"Splunk Enterprise"
]
},
"description": "This search creates permutations of your existing domains, removes the valid domain names and stores them in a specified lookup file so they can be checked for in the associated detection searches.",
"eli5": "This search starts with the dnstwist command consuming domains from a file called domains.csv in the DA-ESS-SOC/lookups directory. This search then adds a domain\\_abuse=true term to each permutation, removes all the valid domain names and stores all that information into a lookup file that is used in the associated detection search. Alternatively domain dnstwist permutations can be calculated from domains in the `cim_corporate_email_domains.csv` and `cim_corporate_web_domains.csv` lookups located in **Splunk\\_SA\\_CIM** using argument `populate_from_cim=true`. Also an individual domain can be passed using argument `domain=<domain>`",
"how_to_implement": "To successfully implement this search you need to update the file called domains.csv in the DA-ESS-SOC/lookup directory. Or `cim_corporate_email_domains.csv` and `cim_corporate_web_domains.csv` from **Splunk\\_SA\\_CIM**.",
"id": "19f7d2ec-6028-4d01-bcdb-bda9a034c17f",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "jhernandez@splunk.com",
"name": "Jose Hernandez"
}
],
"modification_date": "2018-10-08",
"name": "DNSTwist Domain Names",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
baselines/discover_dns_records.json
+50 -50
View File
@@ -1,53 +1,53 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "0 0 1 * *",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| inputlookup cim_corporate_email_domains.csv | inputlookup append=T cim_corporate_web_domains.csv | inputlookup append=T cim_cloud_domains.csv | eval domain = trim(replace(domain, \"\\*\", \"\")) | join domain [|tstats summariesonly=true 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 \"(?<domain>\\w+\\.\\w+?)(?:$|/)\"] | makemv delim=\" \" answer | makemv delim=\" \" type | sort -count | table count,domain,type,query,answer | outputlookup createinapp=true discovered_dns_records.csv"
}
},
"creation_date": "2019-02-14",
"data_metadata": {
"data_models": [
"Network_Resolution"
],
"data_source": [
"Network Communication"
],
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "The search takes corporate and common cloud provider domains configured under `cim_corporate_email_domains.csv`, `cim_corporate_web_domains.csv`, and `cloud_domains.csv` finds their responses across the last 30 days from data in the `Network_Resolution ` datamodel, then stores the output under the `discovered_dns_records.csv` lookup",
"eli5": "Discover the DNS records and their answers for domains owned by the company using network traffic events. The discovered events are exported as a lookup named `discovered_dns_records.csv`",
"entities": [
"query",
"answer"
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "0 0 1 * *",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| inputlookup cim_corporate_email_domains.csv | inputlookup append=T cim_corporate_web_domains.csv | inputlookup append=T cim_cloud_domains.csv | eval domain = trim(replace(domain, \"\\*\", \"\")) | join domain [|tstats summariesonly=true count values(DNS.record_type) as type, values(DNS.answer) as answer from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!=\"unknown\" DNS.answer!=\"\" by DNS.query | rename DNS.query as query | where query!=\"unknown\" | rex field=query \"(?<domain>\\w+\\.\\w+?)(?:$|/)\"] | makemv delim=\" \" answer | makemv delim=\" \" type | sort -count | table count,domain,type,query,answer | outputlookup createinapp=true discovered_dns_records.csv"
}
},
"creation_date": "2019-02-14",
"data_metadata": {
"data_models": [
"Network_Resolution"
],
"how_to_implement": "To successfully implement this search, you must be ingesting DNS logs, and populating the Network_Resolution data model. Also make sure that the cim_corporate_web_domains and cim_corporate_email_domains lookups are populated with the domains owned by your corporation",
"id": "c096f721-8842-42ce-bfc7-74bd8c72b7c3",
"known_false_positives": "Please vet the lookup created by this baseline search ",
"maintainers": [
{
"company": "Splunk",
"email": "jhernandez@splunk.com",
"name": "Jose Hernandez"
}
"data_source": [
"Network Communication"
],
"modification_date": "2019-02-14",
"name": "Discover DNS records",
"original_authors": [
{
"company": "Splunk",
"email": "jhernandez@splunk.com",
"name": "Jose Hernandez"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "The search takes corporate and common cloud provider domains configured under `cim_corporate_email_domains.csv`, `cim_corporate_web_domains.csv`, and `cloud_domains.csv` finds their responses across the last 30 days from data in the `Network_Resolution ` datamodel, then stores the output under the `discovered_dns_records.csv` lookup",
"eli5": "Discover the DNS records and their answers for domains owned by the company using network traffic events. The discovered events are exported as a lookup named `discovered_dns_records.csv`",
"entities": [
"query",
"answer"
],
"how_to_implement": "To successfully implement this search, you must be ingesting DNS logs, and populating the Network_Resolution data model. Also make sure that the cim_corporate_web_domains and cim_corporate_email_domains lookups are populated with the domains owned by your corporation",
"id": "c096f721-8842-42ce-bfc7-74bd8c72b7c3",
"known_false_positives": "Please vet the lookup created by this baseline search ",
"maintainers": [
{
"company": "Splunk",
"email": "jhernandez@splunk.com",
"name": "Jose Hernandez"
}
],
"modification_date": "2019-02-14",
"name": "Discover DNS records",
"original_authors": [
{
"company": "Splunk",
"email": "jhernandez@splunk.com",
"name": "Jose Hernandez"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/identify_ports_on_network.json
+46 -46
View File
@@ -1,49 +1,49 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count dc(All_Traffic.src) as numberOfUniqueHosts from datamodel=Network_Traffic by All_Traffic.dest_port | `drop_dm_object_name(\"All_Traffic\")` | sort - count"
}
},
"creation_date": "2017-06-24",
"data_metadata": {
"data_models": [
"Network_Traffic"
],
"data_source": [
"Network Communication"
],
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "The search counts the number of times a connection was observed to each destination port, and the number of unique source IPs connecting to them.",
"eli5": "For each port being accessed on the network, this search gives the total number of connections observed, and the number of unique IP addresses making those connections.",
"how_to_implement": "To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model.",
"id": "9f3bae5a-9fe3-49df-8c84-5edc51d84b7f",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count dc(All_Traffic.src) as numberOfUniqueHosts from datamodel=Network_Traffic by All_Traffic.dest_port | `drop_dm_object_name(\"All_Traffic\")` | sort - count"
}
},
"creation_date": "2017-06-24",
"data_metadata": {
"data_models": [
"Network_Traffic"
],
"modification_date": "2017-09-13",
"name": "Count of Unique IPs Connecting to Ports",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"data_source": [
"Network Communication"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "The search counts the number of times a connection was observed to each destination port, and the number of unique source IPs connecting to them.",
"eli5": "For each port being accessed on the network, this search gives the total number of connections observed, and the number of unique IP addresses making those connections.",
"how_to_implement": "To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model.",
"id": "9f3bae5a-9fe3-49df-8c84-5edc51d84b7f",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"modification_date": "2017-09-13",
"name": "Count of Unique IPs Connecting to Ports",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/identify_systems_creating_rdp_traffic.json
+46 -46
View File
@@ -1,49 +1,49 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.src | `drop_dm_object_name(\"All_Traffic\")` | sort - count"
}
},
"creation_date": "2017-04-24",
"data_metadata": {
"data_models": [
"Network_Traffic"
],
"data_source": [
"Network Communication"
],
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "This search counts the numbers of times the system has generated remote desktop traffic.",
"eli5": "This search counts the numbers of times the system has tried to connect to another system on TCP/3389, the default port used for RDP traffic.",
"how_to_implement": "To successfully implement this search, you must ingest network traffic and populate the Network_Traffic data model.",
"id": "5cdda34f-4caf-4128-a713-0837fc48b67a",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.src | `drop_dm_object_name(\"All_Traffic\")` | sort - count"
}
},
"creation_date": "2017-04-24",
"data_metadata": {
"data_models": [
"Network_Traffic"
],
"modification_date": "2017-09-15",
"name": "Identify Systems Creating Remote Desktop Traffic",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"data_source": [
"Network Communication"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "This search counts the numbers of times the system has generated remote desktop traffic.",
"eli5": "This search counts the numbers of times the system has tried to connect to another system on TCP/3389, the default port used for RDP traffic.",
"how_to_implement": "To successfully implement this search, you must ingest network traffic and populate the Network_Traffic data model.",
"id": "5cdda34f-4caf-4128-a713-0837fc48b67a",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"modification_date": "2017-09-15",
"name": "Identify Systems Creating Remote Desktop Traffic",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/identify_systems_receiving_rdp_traffic.json
+46 -46
View File
@@ -1,49 +1,49 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.dest | `drop_dm_object_name(\"All_Traffic\")` | sort - count"
}
},
"creation_date": "2017-04-24",
"data_metadata": {
"data_models": [
"Network_Traffic"
],
"data_source": [
"Network Communication"
],
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "This search counts the numbers of times the system has created remote desktop traffic",
"eli5": "This search counts the numbers of times the system has received a connection to TCP/ 3389, the default port used for RDP traffic.",
"how_to_implement": "To successfully implement this search you must ingest network traffic and populate the Network_Traffic data model. If a system receives a lot of remote desktop traffic, you can apply the category common_rdp_destination to it.",
"id": "baaeea15-fe8a-4090-92c2-5b60943bb608",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.dest | `drop_dm_object_name(\"All_Traffic\")` | sort - count"
}
},
"creation_date": "2017-04-24",
"data_metadata": {
"data_models": [
"Network_Traffic"
],
"modification_date": "2017-09-15",
"name": "Identify Systems Receiving Remote Desktop Traffic",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"data_source": [
"Network Communication"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "This search counts the numbers of times the system has created remote desktop traffic",
"eli5": "This search counts the numbers of times the system has received a connection to TCP/ 3389, the default port used for RDP traffic.",
"how_to_implement": "To successfully implement this search you must ingest network traffic and populate the Network_Traffic data model. If a system receives a lot of remote desktop traffic, you can apply the category common_rdp_destination to it.",
"id": "baaeea15-fe8a-4090-92c2-5b60943bb608",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"modification_date": "2017-09-15",
"name": "Identify Systems Receiving Remote Desktop Traffic",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/identify_systems_using_remote_desktop.json
+49 -49
View File
@@ -1,52 +1,52 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count from datamodel=Endpoint.Processes where Processes.process_name=\"*mstsc.exe*\" by Processes.dest Processes.process_name | `drop_dm_object_name(Processes)` | sort - count"
}
},
"creation_date": "2017-04-18",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system.",
"eli5": "This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system. It does this by looking for the process name in the Endpoint data model.",
"how_to_implement": "To successfully implement this search you must be ingesting endpoint data that records process activity.",
"id": "063dfe9f-b1d7-4254-a16d-1e2e7eadd6a8",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count from datamodel=Endpoint.Processes where Processes.process_name=\"*mstsc.exe*\" by Processes.dest Processes.process_name | `drop_dm_object_name(Processes)` | sort - count"
}
},
"creation_date": "2017-04-18",
"data_metadata": {
"data_models": [
"Endpoint"
],
"modification_date": "2019-04-01",
"name": "Identify Systems Using Remote Desktop",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"data_source": [
"Endpoint Intel"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system.",
"eli5": "This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system. It does this by looking for the process name in the Endpoint data model.",
"how_to_implement": "To successfully implement this search you must be ingesting endpoint data that records process activity.",
"id": "063dfe9f-b1d7-4254-a16d-1e2e7eadd6a8",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"modification_date": "2019-04-01",
"name": "Identify Systems Using Remote Desktop",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/monitor_successful_backups.json
+45 -45
View File
@@ -1,48 +1,48 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=\"netbackup_logs\" \"Disk/Partition backup completed successfully.\" | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE"
}
},
"creation_date": "2017-08-24",
"data_metadata": {
"data_source": [
"Backup Logs"
],
"data_sourcetypes": [
"netbackup_logs"
],
"providing_technologies": [
"Netbackup"
]
},
"description": "This search is intended to give you a feel for how often successful backups are conducted in your environment. Fluctuations in these numbers will allow you to determine when you should investigate.",
"eli5": "This search gives you the count and the hostname of all the systems that had a successful backup each day.",
"how_to_implement": "To successfully implement this search you must be ingesting your backup logs.",
"id": "b4d0dfb2-2195-4f6e-93a3-48468ed9734e",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=\"netbackup_logs\" \"Disk/Partition backup completed successfully.\" | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE"
}
},
"creation_date": "2017-08-24",
"data_metadata": {
"data_source": [
"Backup Logs"
],
"modification_date": "2017-09-12",
"name": "Monitor Successful Backups",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"data_sourcetypes": [
"netbackup_logs"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"Netbackup"
]
},
"description": "This search is intended to give you a feel for how often successful backups are conducted in your environment. Fluctuations in these numbers will allow you to determine when you should investigate.",
"eli5": "This search gives you the count and the hostname of all the systems that had a successful backup each day.",
"how_to_implement": "To successfully implement this search you must be ingesting your backup logs.",
"id": "b4d0dfb2-2195-4f6e-93a3-48468ed9734e",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"modification_date": "2017-09-12",
"name": "Monitor Successful Backups",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/monitor_successful_windows_updates.json
+45 -45
View File
@@ -1,48 +1,48 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` dc(Updates.dest) as count FROM datamodel=Updates where Updates.vendor_product=\"Microsoft Windows\" AND Updates.status=installed by _time span=1d"
}
},
"creation_date": "2017-08-24",
"data_metadata": {
"data_models": [
"Updates"
],
"data_source": [
"Windows Updates"
],
"providing_technologies": [
"Microsoft Windows"
]
},
"description": "This search is intended to give you a feel for how often successful Windows updates are applied in your environments. Fluctuations in these numbers will allow you to determine when you should be concerned.",
"eli5": "This search gives you the count and name of all the systems that had a successful update applied each day",
"how_to_implement": "You must be ingesting your Windows Update Logs",
"id": "6a80535c-86a6-4b54-894c-4b446d0c701d",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` dc(Updates.dest) as count FROM datamodel=Updates where Updates.vendor_product=\"Microsoft Windows\" AND Updates.status=installed by _time span=1d"
}
},
"creation_date": "2017-08-24",
"data_metadata": {
"data_models": [
"Updates"
],
"modification_date": "2017-09-14",
"name": "Windows Updates Install Successes",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"data_source": [
"Windows Updates"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"Microsoft Windows"
]
},
"description": "This search is intended to give you a feel for how often successful Windows updates are applied in your environments. Fluctuations in these numbers will allow you to determine when you should be concerned.",
"eli5": "This search gives you the count and name of all the systems that had a successful update applied each day",
"how_to_implement": "You must be ingesting your Windows Update Logs",
"id": "6a80535c-86a6-4b54-894c-4b446d0c701d",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"modification_date": "2017-09-14",
"name": "Windows Updates Install Successes",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/monitor_unsuccessful_backups.json
+45 -45
View File
@@ -1,48 +1,48 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=\"netbackup_logs\" \"An error occurred, failed to backup.\" | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE"
}
},
"creation_date": "2017-08-24",
"data_metadata": {
"data_source": [
"Backup Logs"
],
"data_sourcetypes": [
"netbackup_logs"
],
"providing_technologies": [
"Netbackup"
]
},
"description": "This search is intended to give you a feel for how often backup failures happen in your environments. Fluctuations in these numbers will allow you to determine when you should investigate.",
"eli5": "This search gives you the count and hostname of all the systems that had a backup failure each day",
"how_to_implement": "To successfully implement this search you must be ingesting your backup logs.",
"id": "b2178fed-592f-492b-b851-74161678aa56",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=\"netbackup_logs\" \"An error occurred, failed to backup.\" | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE"
}
},
"creation_date": "2017-08-24",
"data_metadata": {
"data_source": [
"Backup Logs"
],
"modification_date": "2017-09-12",
"name": "Monitor Unsuccessful Backups",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"data_sourcetypes": [
"netbackup_logs"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"Netbackup"
]
},
"description": "This search is intended to give you a feel for how often backup failures happen in your environments. Fluctuations in these numbers will allow you to determine when you should investigate.",
"eli5": "This search gives you the count and hostname of all the systems that had a backup failure each day",
"how_to_implement": "To successfully implement this search you must be ingesting your backup logs.",
"id": "b2178fed-592f-492b-b851-74161678aa56",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"modification_date": "2017-09-12",
"name": "Monitor Unsuccessful Backups",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/monitor_unsuccessful_windows_updates.json
+45 -45
View File
@@ -1,48 +1,48 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` dc(Updates.dest) as count FROM datamodel=Updates where Updates.vendor_product=\"Microsoft Windows\" AND Updates.status=failure by _time span=1d"
}
},
"creation_date": "2017-08-24",
"data_metadata": {
"data_models": [
"Updates"
],
"data_source": [
"Windows Updates"
],
"providing_technologies": [
"Microsoft Windows"
]
},
"description": "This search is intended to give you a feel for how often Windows updates fail to install in your environment. Fluctuations in these numbers will allow you to determine when you should be concerned.",
"eli5": "This search gives you the count of the number of systems that attempted and failed to install a Windows update each day.",
"how_to_implement": "You must be ingesting your Windows Update Logs",
"id": "6a4dbd1b-4502-4a11-943a-82b5ae7a42d7",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` dc(Updates.dest) as count FROM datamodel=Updates where Updates.vendor_product=\"Microsoft Windows\" AND Updates.status=failure by _time span=1d"
}
},
"creation_date": "2017-08-24",
"data_metadata": {
"data_models": [
"Updates"
],
"modification_date": "2017-09-14",
"name": "Windows Updates Install Failures",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"data_source": [
"Windows Updates"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"Microsoft Windows"
]
},
"description": "This search is intended to give you a feel for how often Windows updates fail to install in your environment. Fluctuations in these numbers will allow you to determine when you should be concerned.",
"eli5": "This search gives you the count of the number of systems that attempted and failed to install a Windows update each day.",
"how_to_implement": "You must be ingesting your Windows Update Logs",
"id": "6a4dbd1b-4502-4a11-943a-82b5ae7a42d7",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"modification_date": "2017-09-14",
"name": "Windows Updates Install Failures",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/previously_seen_aws_cross_account_activity.json
+45 -45
View File
@@ -1,48 +1,48 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventName=AssumeRole | spath output=requestingAccountId path=userIdentity.accountId | spath output=requestedAccountId path=resources{}.accountId | search requestingAccountId=* | where requestingAccountId!=requestedAccountId | stats earliest(_time) as firstTime latest(_time) as lastTime by requestingAccountId, requestedAccountId | outputlookup previously_seen_aws_cross_account_activity | stats count"
}
},
"creation_date": "2018-06-04",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for <b>AssumeRole</b> events where the requesting account differs from the requested account, then writes these relationships to a lookup file.",
"eli5": "In this support search, we look for <b>AssumeRole</b> events where the requesting account is different from the requested account. The first and last times these events are seen are written to a lookup file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Validate the user name entries in <code>previously_seen_aws_cross_account_activity.csv</code>, a lookup file created by this support search.",
"id": "1cc22b09-c867-416e-a511-cb36ac44aee2",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventName=AssumeRole | spath output=requestingAccountId path=userIdentity.accountId | spath output=requestedAccountId path=resources{}.accountId | search requestingAccountId=* | where requestingAccountId!=requestedAccountId | stats earliest(_time) as firstTime latest(_time) as lastTime by requestingAccountId, requestedAccountId | outputlookup previously_seen_aws_cross_account_activity | stats count"
}
},
"creation_date": "2018-06-04",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"modification_date": "2018-06-04",
"name": "Previously Seen AWS Cross Account Activity",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"data_sourcetypes": [
"aws:cloudtrail"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file.",
"eli5": "In this support search, we look for **AssumeRole** events where the requesting account is different from the requested account. The first and last times these events are seen are written to a lookup file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Validate the user name entries in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this support search.",
"id": "1cc22b09-c867-416e-a511-cb36ac44aee2",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"modification_date": "2018-06-04",
"name": "Previously Seen AWS Cross Account Activity",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/previously_seen_aws_users.json
+59 -59
View File
@@ -1,62 +1,62 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "0 0 1 * *",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation src | eval City=if(City LIKE \"\",src,City),Region=if(Region LIKE \"\",src,Region) | stats earliest(_time) as earliest latest(_time) as latest by user src City Region Country | outputlookup previously_seen_users_console_logins.csv | stats count"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "0 0 1 * *",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation src | eval City=if(City LIKE \"\",src,City),Region=if(Region LIKE \"\",src,Region) | stats earliest(_time) as earliest latest(_time) as latest by user src City Region Country | outputlookup previously_seen_users_console_logins.csv | stats count"
}
},
"creation_date": "2018-02-23",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for CloudTrail events where a user logs into the console, then creates a baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last 30 days.",
"eli5": "In this support search, we look for console login events by a particular user and create an initial baseline cache for the previous 30 days, including the earliest and latest times, City, Region, and Country a particular user ARN is seen in our dataset, grouped by the ARN value. In cases where City and Region cannot be determined, the source IP address is substituted for these values.",
"entities": [
"user",
"src"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user name entries in `previously_seen_users_console_logins.csv`, which is a lookup file created as a result of running this support search.",
"id": "fc0edc95-ff2b-48b0-9f6f-63da3789fd03",
"known_false_positives": "n/a",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
},
"creation_date": "2018-02-23",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
}
],
"modification_date": "2018-04-30",
"name": "Previously seen users in CloudTrail",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
},
"description": "This search looks for CloudTrail events where a user logs into the console, then creates a baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last 30 days.",
"eli5": "In this support search, we look for console login events by a particular user and create an initial baseline cache for the previous 30 days, including the earliest and latest times, City, Region, and Country a particular user ARN is seen in our dataset, grouped by the ARN value. In cases where City and Region cannot be determined, the source IP address is substituted for these values.",
"entities": [
"user",
"src"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user name entries in <code>previously_seen_users_console_logins.csv</code>, which is a lookup file created as a result of running this support search.",
"id": "fc0edc95-ff2b-48b0-9f6f-63da3789fd03",
"known_false_positives": "n/a",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
},
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
}
],
"modification_date": "2018-04-30",
"name": "Previously seen users in CloudTrail",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
},
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.1"
}
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.1"
}
baselines/previously_seen_cmd_arguments.json
+49 -49
View File
@@ -1,52 +1,52 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = \"* /c *\" by Processes.process | `drop_dm_object_name(Processes)`"
}
},
"creation_date": "2018-04-09",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search looks for command-line arguments where <code>cmd.exe /c</code> is used to execute a program, then creates a baseline of the earliest and latest times we have encountered this command-line argument in our dataset within the last 30 days.",
"eli5": "In this support search, we look for command-line arguments using the parameter <code>/c</code> to execute processes and create an initial baseline cache for the previous 30 days. This will include the earliest and latest times a particular command-line argument is seen in our dataset, grouped by the command-line value.",
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "fc0edc95-ff2b-48b0-9f6f-63da3789fd23",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = \"* /c *\" by Processes.process | `drop_dm_object_name(Processes)`"
}
},
"creation_date": "2018-04-09",
"data_metadata": {
"data_models": [
"Endpoint"
],
"modification_date": "2019-03-01",
"name": "Previously seen command line arguments",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
"data_source": [
"Endpoint Intel"
],
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search looks for command-line arguments where `cmd.exe /c` is used to execute a program, then creates a baseline of the earliest and latest times we have encountered this command-line argument in our dataset within the last 30 days.",
"eli5": "In this support search, we look for command-line arguments using the parameter `/c` to execute processes and create an initial baseline cache for the previous 30 days. This will include the earliest and latest times a particular command-line argument is seen in our dataset, grouped by the command-line value.",
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "fc0edc95-ff2b-48b0-9f6f-63da3789fd23",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"modification_date": "2019-03-01",
"name": "Previously seen command line arguments",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
baselines/previously_seen_ec2_amis.json
+45 -45
View File
@@ -1,48 +1,48 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-90d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | rename requestParameters.instancesSet.items{}.imageId as amiID | stats earliest(_time) as earliest latest(_time) as latest by amiID | outputlookup previously_seen_ec2_amis.csv | stats count"
}
},
"creation_date": "2018-03-12",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search builds a table of previously seen AMIs used to launch EC2 instances",
"eli5": "In this support search, we create a table of the earliest and latest time that a specific AMI ID has been seen. This table is then outputted to a csv file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.",
"id": "bb1bd99d-1e93-45f1-9571-cfed42d372b9",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-90d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | rename requestParameters.instancesSet.items{}.imageId as amiID | stats earliest(_time) as earliest latest(_time) as latest by amiID | outputlookup previously_seen_ec2_amis.csv | stats count"
}
},
"creation_date": "2018-03-12",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"modification_date": "2018-03-12",
"name": "Previously Seen EC2 AMIs",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"data_sourcetypes": [
"aws:cloudtrail"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"AWS"
]
},
"description": "This search builds a table of previously seen AMIs used to launch EC2 instances",
"eli5": "In this support search, we create a table of the earliest and latest time that a specific AMI ID has been seen. This table is then outputted to a csv file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.",
"id": "bb1bd99d-1e93-45f1-9571-cfed42d372b9",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"modification_date": "2018-03-12",
"name": "Previously Seen EC2 AMIs",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/previously_seen_ec2_instance_types.json
+45 -45
View File
@@ -1,48 +1,48 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-90d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | rename requestParameters.instanceType as instanceType | fillnull value=\"m1.small\" instanceType | stats earliest(_time) as earliest latest(_time) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | stats count"
}
},
"creation_date": "2018-03-08",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search builds a table of previously seen EC2 instance types",
"eli5": "In this support search, we create a table of the earliest and latest time that a specific EC2 instance type has been seen. The instanceType request field is not required and defaults to m1.small, so any time this field is null, the search defaults the field to m1.small. This table is then outputted to a csv file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.",
"id": "b8f029f2-65a6-4d76-be98-dad1c9d59c45",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-90d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | rename requestParameters.instanceType as instanceType | fillnull value=\"m1.small\" instanceType | stats earliest(_time) as earliest latest(_time) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | stats count"
}
},
"creation_date": "2018-03-08",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"modification_date": "2018-03-08",
"name": "Previously Seen EC2 Instance Types",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"data_sourcetypes": [
"aws:cloudtrail"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"AWS"
]
},
"description": "This search builds a table of previously seen EC2 instance types",
"eli5": "In this support search, we create a table of the earliest and latest time that a specific EC2 instance type has been seen. The instanceType request field is not required and defaults to m1.small, so any time this field is null, the search defaults the field to m1.small. This table is then outputted to a csv file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.",
"id": "b8f029f2-65a6-4d76-be98-dad1c9d59c45",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"modification_date": "2018-03-08",
"name": "Previously Seen EC2 Instance Types",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/previously_seen_ec2_launches_by_user.json
+45 -45
View File
@@ -1,48 +1,48 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-90d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | rename userIdentity.arn as arn | stats earliest(_time) as firstTime latest(_time) as lastTime by arn | outputlookup previously_seen_ec2_launches_by_user.csv | stats count"
}
},
"creation_date": "2018-03-15",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search builds a table of previously seen ARNs that have launched a EC2 instance.",
"eli5": "In this support search, we create a table of the earliest and latest times that an ARN has launched a EC2 instance. This table is then outputted to a csv file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.",
"id": "6c767ac0-0906-4355-9a83-927f5ee7bdad",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-90d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | rename userIdentity.arn as arn | stats earliest(_time) as firstTime latest(_time) as lastTime by arn | outputlookup previously_seen_ec2_launches_by_user.csv | stats count"
}
},
"creation_date": "2018-03-15",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"modification_date": "2018-03-15",
"name": "Previously Seen EC2 Launches By User",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"data_sourcetypes": [
"aws:cloudtrail"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"AWS"
]
},
"description": "This search builds a table of previously seen ARNs that have launched a EC2 instance.",
"eli5": "In this support search, we create a table of the earliest and latest times that an ARN has launched a EC2 instance. This table is then outputted to a csv file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.",
"id": "6c767ac0-0906-4355-9a83-927f5ee7bdad",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"modification_date": "2018-03-15",
"name": "Previously Seen EC2 Launches By User",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/previously_seen_ec2_modifications.json
+45 -45
View File
@@ -1,48 +1,48 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-90d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail `ec2ModificationAPIs` errorCode=success | spath output=arn userIdentity.arn | stats earliest(_time) as firstTime latest(_time) as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user | stats count"
}
},
"creation_date": "2018-04-05",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search builds a table of previously seen ARNs that have launched a EC2 instance.",
"eli5": "In this support search, we create a table of the earliest and latest times that an ARN has modified a EC2 instance. The list of APIs that modify an EC2 are defined in the <code>ec2ModificationAPIs</code> macro for ease of use. This table is then outputted to a file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove APIs that modify an EC2 instance, edit the macro <code>ec2ModificationAPIs</code>.",
"id": "4d69091b-d975-4267-85df-888bd41034eb",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-90d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail `ec2ModificationAPIs` errorCode=success | spath output=arn userIdentity.arn | stats earliest(_time) as firstTime latest(_time) as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user | stats count"
}
},
"creation_date": "2018-04-05",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"modification_date": "2018-04-05",
"name": "Previously Seen EC2 Modifications By User",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"data_sourcetypes": [
"aws:cloudtrail"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"AWS"
]
},
"description": "This search builds a table of previously seen ARNs that have launched a EC2 instance.",
"eli5": "In this support search, we create a table of the earliest and latest times that an ARN has modified a EC2 instance. The list of APIs that modify an EC2 are defined in the `ec2ModificationAPIs` macro for ease of use. This table is then outputted to a file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2ModificationAPIs`.",
"id": "4d69091b-d975-4267-85df-888bd41034eb",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"modification_date": "2018-04-05",
"name": "Previously Seen EC2 Modifications By User",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/previously_seen_provisioning_activity_src.json
+45 -45
View File
@@ -1,48 +1,48 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-90d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats count"
}
},
"creation_date": "2018-03-16",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search builds a table of the first and last times seen for every IP address (along with its physical location) previously associated with cloud-provisioning activity. This is broadly defined as any event that runs or creates something.",
"eli5": "This search includes any event name that begins with \"run\" or \"create,\" and then determines the first and last time these events were seen for each IP address that initiated the action. The search then consults a <b>GeoIP</b> database to determine the physical location of this IP address. This table outputs to a file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.",
"id": "ac88e6a0-4fba-4dfd-b7b9-8964df7d1aee",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-90d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats count"
}
},
"creation_date": "2018-03-16",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"modification_date": "2018-03-16",
"name": "Previously Seen AWS Provisioning Activity Sources",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"data_sourcetypes": [
"aws:cloudtrail"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"AWS"
]
},
"description": "This search builds a table of the first and last times seen for every IP address (along with its physical location) previously associated with cloud-provisioning activity. This is broadly defined as any event that runs or creates something.",
"eli5": "This search includes any event name that begins with \"run\" or \"create,\" and then determines the first and last time these events were seen for each IP address that initiated the action. The search then consults a **GeoIP** database to determine the physical location of this IP address. This table outputs to a file.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.",
"id": "ac88e6a0-4fba-4dfd-b7b9-8964df7d1aee",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"modification_date": "2018-03-16",
"name": "Previously Seen AWS Provisioning Activity Sources",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/previously_seen_s3_remote_ip.json
+45 -45
View File
@@ -1,48 +1,48 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:s3:accesslogs http_status=200 | stats earliest(_time) as earliest latest(_time) as latest by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip | stats count"
}
},
"creation_date": "2018-06-28",
"data_metadata": {
"data_source": [
"AWS S3 Access logs"
],
"data_sourcetypes": [
"aws:s3:accesslogs"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for successful access to S3 buckets from remote IP addresses, then creates a baseline of the earliest and latest times we have encountered this remote IP within the last 30 days. In this support search, we are only looking for S3 access events where the HTTP response code from AWS is \"200\"",
"eli5": "In this support search, we are looking for successful S3 bucket-access attempts made from remote IPs. The intent is to create an initial baseline cache of remote IP addresses per bucket name for the previous 30 days--including the earliest and latest times seen in our dataset--grouped by the value of remote IP and the name of the S3 bucket.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access-logs inputs. You must validate the remote IP and bucket name entries in <code>previously_seen_S3_access_from_remote_ip.csv</code>, which is a lookup file created as a result of running this support search.",
"id": "fc0edc15-fq2c-48b0-9f6f-63qa1281fd03",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:s3:accesslogs http_status=200 | stats earliest(_time) as earliest latest(_time) as latest by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip | stats count"
}
},
"creation_date": "2018-06-28",
"data_metadata": {
"data_source": [
"AWS S3 Access logs"
],
"modification_date": "2018-06-28",
"name": "Previously seen S3 bucket access by remote IP",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
"data_sourcetypes": [
"aws:s3:accesslogs"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for successful access to S3 buckets from remote IP addresses, then creates a baseline of the earliest and latest times we have encountered this remote IP within the last 30 days. In this support search, we are only looking for S3 access events where the HTTP response code from AWS is \"200\"",
"eli5": "In this support search, we are looking for successful S3 bucket-access attempts made from remote IPs. The intent is to create an initial baseline cache of remote IP addresses per bucket name for the previous 30 days--including the earliest and latest times seen in our dataset--grouped by the value of remote IP and the name of the S3 bucket.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access-logs inputs. You must validate the remote IP and bucket name entries in `previously_seen_S3_access_from_remote_ip.csv`, which is a lookup file created as a result of running this support search.",
"id": "fc0edc15-fq2c-48b0-9f6f-63qa1281fd03",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"modification_date": "2018-06-28",
"name": "Previously seen S3 bucket access by remote IP",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/previously_seen_user_roles.json
+45 -45
View File
@@ -1,48 +1,48 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole | stats earliest(_time) as earliest latest(_time) as latest by userName eventName | outputlookup previously_seen_api_calls_from_user_roles | stats count"
}
},
"creation_date": "2018-04-01",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for successful API calls made by different user roles, then creates a baseline of the earliest and latest times we have encountered this user role. It also returns the name of the API call in our dataset--grouped by user role and name of the API call--that occurred within the last 30 days. In this support search, we are only looking for events where the user identity is Assumed Role.",
"eli5": "In this support search, we are looking for successful API calls made by user roles within your AWS infrastructure. The intent is to create an initial baseline cache of names of the API calls per security role for the previous 30 days--including the earliest and latest times seen in our dataset--grouped by the value of user role and the name of the API call. It is also worth noting that the role of a particular user is parsed as \"userName\" in the CloudTrail logs.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user role entries in <code>previously_seen_api_calls_from_user_roles.csv</code>, which is a lookup file created as a result of running this support search.",
"id": "fc0edc95-fq2c-48b0-9f6f-63da3289fd03",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole | stats earliest(_time) as earliest latest(_time) as latest by userName eventName | outputlookup previously_seen_api_calls_from_user_roles | stats count"
}
},
"creation_date": "2018-04-01",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"modification_date": "2018-04-16",
"name": "Previously seen API call per user roles in CloudTrail",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
"data_sourcetypes": [
"aws:cloudtrail"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for successful API calls made by different user roles, then creates a baseline of the earliest and latest times we have encountered this user role. It also returns the name of the API call in our dataset--grouped by user role and name of the API call--that occurred within the last 30 days. In this support search, we are only looking for events where the user identity is Assumed Role.",
"eli5": "In this support search, we are looking for successful API calls made by user roles within your AWS infrastructure. The intent is to create an initial baseline cache of names of the API calls per security role for the previous 30 days--including the earliest and latest times seen in our dataset--grouped by the value of user role and the name of the API call. It is also worth noting that the role of a particular user is parsed as \"userName\" in the CloudTrail logs.",
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user role entries in `previously_seen_api_calls_from_user_roles.csv`, which is a lookup file created as a result of running this support search.",
"id": "fc0edc95-fq2c-48b0-9f6f-63da3289fd03",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"modification_date": "2018-04-16",
"name": "Previously seen API call per user roles in CloudTrail",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/previously_seen_windows_service_starts.json
+45 -45
View File
@@ -1,48 +1,48 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "eventtype=wineventlog_system signature_id=7036 | rex field=Message \"The (?<serviceName>[\\w\\s-]*) service entered the (?<action>\\w*) state\" | where action=\"running\" | stats earliest(_time) as firstTime, latest(_time) as lastTime by serviceName | outputlookup previously_seen_running_windows_services | stats count"
}
},
"creation_date": "2018-07-20",
"data_metadata": {
"data_eventtypes": [
"wineventlog_system"
],
"data_source": [
"Windows Event Logs"
],
"providing_technologies": [
"Microsoft Windows"
]
},
"description": "This collects the services that have been started across your entire enterprise.",
"eli5": "In this support search, we look for Windows system-event code that indicates a status change of a Windows service. It extracts both the name of the service and the action taken by the service from the logs. It keeps only services that have entered the running state. Finally, it finds the first time the service has been seen running across the enterprise and writes that file to a lookup table.",
"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.",
"id": "64ce0ade-cb01-4678-bddd-d31c0b175394",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "eventtype=wineventlog_system signature_id=7036 | rex field=Message \"The (?<serviceName>[\\w\\s-]*) service entered the (?<action>\\w*) state\" | where action=\"running\" | stats earliest(_time) as firstTime, latest(_time) as lastTime by serviceName | outputlookup previously_seen_running_windows_services | stats count"
}
},
"creation_date": "2018-07-20",
"data_metadata": {
"data_eventtypes": [
"wineventlog_system"
],
"modification_date": "2019-02-27",
"name": "Previously Seen Running Windows Services",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"data_source": [
"Windows Event Logs"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"Microsoft Windows"
]
},
"description": "This collects the services that have been started across your entire enterprise.",
"eli5": "In this support search, we look for Windows system-event code that indicates a status change of a Windows service. It extracts both the name of the service and the action taken by the service from the logs. It keeps only services that have entered the running state. Finally, it finds the first time the service has been seen running across the enterprise and writes that file to a lookup table.",
"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.",
"id": "64ce0ade-cb01-4678-bddd-d31c0b175394",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"modification_date": "2019-02-27",
"name": "Previously Seen Running Windows Services",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/systems_ready_for_spectre_meltdown_patch.json
+49 -49
View File
@@ -1,52 +1,52 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-1d@d",
"latest_time": "-10m@m"
},
"search": "| tstats `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 | `ctime(lastTime)` | `ctime(firstTime)` | `drop_dm_object_name(\"All_Changes\")`"
}
},
"creation_date": "2018-01-08",
"data_metadata": {
"data_models": [
"Change_Analysis"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "Some AV applications can cause the Spectre/Meltdown patch for Windows not to install successfully. This registry key is supposed to be created by the AV engine when it has been patched to be able to handle the Windows patch. If this key has been written, the system can then be patched for Spectre and Meltdown.",
"eli5": "This search looks to see if a registry key was created at <code>HKLM\\Software\\Microsoft\\Windows\\CurrentVersion\\QualityCompat</code>. It will tell you when it was created and, if possible, what process created it.",
"how_to_implement": "You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.",
"id": "fc0edc95-ff2b-48b0-9f6f-63da3789fd61",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-1d@d",
"latest_time": "-10m@m"
},
"search": "| tstats `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 | `ctime(lastTime)` | `ctime(firstTime)` | `drop_dm_object_name(\"All_Changes\")`"
}
},
"creation_date": "2018-01-08",
"data_metadata": {
"data_models": [
"Change_Analysis"
],
"modification_date": "2018-01-08",
"name": "Systems Ready for Spectre-Meltdown Windows Patch",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"data_source": [
"Endpoint Intel"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "Some AV applications can cause the Spectre/Meltdown patch for Windows not to install successfully. This registry key is supposed to be created by the AV engine when it has been patched to be able to handle the Windows patch. If this key has been written, the system can then be patched for Spectre and Meltdown.",
"eli5": "This search looks to see if a registry key was created at `HKLM\\Software\\Microsoft\\Windows\\CurrentVersion\\QualityCompat`. It will tell you when it was created and, if possible, what process created it.",
"how_to_implement": "You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.",
"id": "fc0edc95-ff2b-48b0-9f6f-63da3789fd61",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"modification_date": "2018-01-08",
"name": "Systems Ready for Spectre-Meltdown Windows Patch",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/train_dns_query_length.json
+46 -46
View File
@@ -1,49 +1,49 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-31d@d",
"latest_time": "-1d@d"
},
"search": "| tstats `summariesonly` count from datamodel=Network_Resolution by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name(\"DNS\")` | eval query_length = len(query) | fit DensityFunction query_length by record_type into dns_query_pdfmodel"
}
},
"creation_date": "2019-05-08",
"data_metadata": {
"data_models": [
"Network_Resolution"
],
"data_source": [
"DNS"
],
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the DNS queries for each DNS record type observed in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which uses it to identify outliers in the length of the DNS query.",
"eli5": "Create a machine-learning (ML) model to characterize the length of DNS requests seen in your environment to help identify unusually long ones that may be indicative of attacker infrastrucutre or the use of DNS as a command-and-control channel in your environment.",
"how_to_implement": "To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any 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`.",
"id": "c914844c-0ff5-4efc-8d44-c063443129ba",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-31d@d",
"latest_time": "-1d@d"
},
"search": "| tstats `summariesonly` count from datamodel=Network_Resolution by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name(\"DNS\")` | eval query_length = len(query) | fit DensityFunction query_length by record_type into dns_query_pdfmodel"
}
},
"creation_date": "2019-05-08",
"data_metadata": {
"data_models": [
"Network_Resolution"
],
"modification_date": "2019-05-08",
"name": "Baseline of DNS Query Length - MLTK",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
"data_source": [
"DNS"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the DNS queries for each DNS record type observed in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which uses it to identify outliers in the length of the DNS query.",
"eli5": "Create a machine-learning (ML) model to characterize the length of DNS requests seen in your environment to help identify unusually long ones that may be indicative of attacker infrastrucutre or the use of DNS as a command-and-control channel in your environment.",
"how_to_implement": "To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any 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`.",
"id": "c914844c-0ff5-4efc-8d44-c063443129ba",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"modification_date": "2019-05-08",
"name": "Baseline of DNS Query Length - MLTK",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/train_smb_traffic_spike.json
+46 -46
View File
@@ -1,49 +1,49 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-31d@d",
"latest_time": "-1d@d"
},
"search": "| tstats `summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=10m, All_Traffic.src | eval HourOfDay=strftime(_time, \"%H\") | eval DayOfWeek=strftime(_time, \"%A\") | `drop_dm_object_name(\"All_Traffic\")` | fit DensityFunction count by \"HourOfDay,DayOfWeek\" into smb_pdfmodel"
}
},
"creation_date": "2019-05-08",
"data_metadata": {
"data_models": [
"Network_Traffic"
],
"data_source": [
"Network Communications"
],
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the number of SMB connections observed each hour for every day of week. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search to identify outliers in the number of SMB connections for that hour and day of the week.",
"eli5": "Create a machine-learning (ML) model to characterize the number of SMB connections observed in your environment. This may help identify spikes in SMB traffic that may be indicative of attackers scanning or attempting to propagate to other systems in your environment. By default, this model is built over 30 days of data and profiles the number of SMB connections in your environment by the hour of day/day of week that the connections occur.",
"how_to_implement": "You must be ingesting network traffic and populating the Network_Traffic data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. To improve your results, 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`.",
"id": "df98763b-0b08-4281-8ef9-08db7ac572a9",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-31d@d",
"latest_time": "-1d@d"
},
"search": "| tstats `summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=10m, All_Traffic.src | eval HourOfDay=strftime(_time, \"%H\") | eval DayOfWeek=strftime(_time, \"%A\") | `drop_dm_object_name(\"All_Traffic\")` | fit DensityFunction count by \"HourOfDay,DayOfWeek\" into smb_pdfmodel"
}
},
"creation_date": "2019-05-08",
"data_metadata": {
"data_models": [
"Network_Traffic"
],
"modification_date": "2019-05-08",
"name": "Baseline of SMB Traffic - MLTK",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
"data_source": [
"Network Communications"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the number of SMB connections observed each hour for every day of week. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search to identify outliers in the number of SMB connections for that hour and day of the week.",
"eli5": "Create a machine-learning (ML) model to characterize the number of SMB connections observed in your environment. This may help identify spikes in SMB traffic that may be indicative of attackers scanning or attempting to propagate to other systems in your environment. By default, this model is built over 30 days of data and profiles the number of SMB connections in your environment by the hour of day/day of week that the connections occur.",
"how_to_implement": "You must be ingesting network traffic and populating the Network_Traffic data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. To improve your results, 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`.",
"id": "df98763b-0b08-4281-8ef9-08db7ac572a9",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"modification_date": "2019-05-08",
"name": "Baseline of SMB Traffic - MLTK",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/train_unusually_long_commandlines.json
+49 -49
View File
@@ -1,52 +1,52 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-31d@d",
"latest_time": "-1d@d"
},
"search": "| tstats `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 | `ctime(start_time)`| `ctime(end_time)`| eval processlen=len(process) | fit DensityFunction processlen by user into cmdline_pdfmodel"
}
},
"creation_date": "2019-05-08",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the command lines observed for each user in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies outliers in the length of the command line.",
"eli5": "Create a machine-learning (ML) model to characterize the length of the command lines used in your environment. This can help you identify unusually long ones that may indicate that attackers are executing commands on yout systems.",
"how_to_implement": "You must be ingesting endpoint data and populating the Endpoint data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.",
"id": "d2a4d85b-fc6a-47a0-82f6-bc1ec2ebc459",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "",
"earliest_time": "-31d@d",
"latest_time": "-1d@d"
},
"search": "| tstats `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 | `ctime(start_time)`| `ctime(end_time)`| eval processlen=len(process) | fit DensityFunction processlen by user into cmdline_pdfmodel"
}
},
"creation_date": "2019-05-08",
"data_metadata": {
"data_models": [
"Endpoint"
],
"modification_date": "2019-05-08",
"name": "Baseline of Command Line Length - MLTK",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
"data_source": [
"Endpoint Intel"
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the command lines observed for each user in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies outliers in the length of the command line.",
"eli5": "Create a machine-learning (ML) model to characterize the length of the command lines used in your environment. This can help you identify unusually long ones that may indicate that attackers are executing commands on yout systems.",
"how_to_implement": "You must be ingesting endpoint data and populating the Endpoint data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.",
"id": "d2a4d85b-fc6a-47a0-82f6-bc1ec2ebc459",
"known_false_positives": "",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"modification_date": "2019-05-08",
"name": "Baseline of Command Line Length - MLTK",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
baselines/update_previously_seen_aws_users.json
+49 -49
View File
@@ -1,52 +1,52 @@
{
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "0 0 1 * *",
"earliest_time": "-60m@m",
"latest_time": "m@m"
},
"search": "sourcetype=aws:cloudtrail eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation src | eval City=if(City LIKE \"\",src,City),Region=if(Region LIKE \"\",src,Region) | stats earliest(_time) AS earliest latest(_time) AS latest by user src City Region Country | inputlookup append=t previously_seen_users_console_logins.csv | stats min(earliest) as earliest max(latest) as latest by user src City Region Country | outputlookup previously_seen_users_console_logins.csv"
}
},
"creation_date": "2019-04-25",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for CloudTrail events where a user logs into the console, then updates the baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last hour.",
"eli5": "In this support search, we look for console login events by a particular user to update the baseline cache of users/arns making the accesses, including the earliest and latest times, City, Region, and Country a particular user ARN is seen in our dataset, grouped by the ARN value. In cases where City and Region cannot be determined, the source IP address is substituted for these values.",
"entities": [
"user",
"src"
"baseline": {
"splunk": {
"schedule": {
"cron_schedule": "0 0 1 * *",
"earliest_time": "-60m@m",
"latest_time": "m@m"
},
"search": "sourcetype=aws:cloudtrail eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation src | eval City=if(City LIKE \"\",src,City),Region=if(Region LIKE \"\",src,Region) | stats earliest(_time) AS earliest latest(_time) AS latest by user src City Region Country | inputlookup append=t previously_seen_users_console_logins.csv | stats min(earliest) as earliest max(latest) as latest by user src City Region Country | outputlookup previously_seen_users_console_logins.csv"
}
},
"creation_date": "2019-04-25",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"how_to_implement": "You must install the AWS App for 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 <code>previously_seen_users_console_logins.csv</code>, which is a lookup file created as a result of running this support search.",
"id": "06c036e6-d6d7-4daa-bd76-411c3d356031",
"known_false_positives": "n/a",
"maintainers": [
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
}
"data_sourcetypes": [
"aws:cloudtrail"
],
"modification_date": "2018-04-30",
"name": "Update previously seen users in CloudTrail",
"original_authors": [
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for CloudTrail events where a user logs into the console, then updates the baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last hour.",
"eli5": "In this support search, we look for console login events by a particular user to update the baseline cache of users/arns making the accesses, including the earliest and latest times, City, Region, and Country a particular user ARN is seen in our dataset, grouped by the ARN value. In cases where City and Region cannot be determined, the source IP address is substituted for these values.",
"entities": [
"user",
"src"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user name entries in `previously_seen_users_console_logins.csv`, which is a lookup file created as a result of running this support search.",
"id": "06c036e6-d6d7-4daa-bd76-411c3d356031",
"known_false_positives": "n/a",
"maintainers": [
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
}
],
"modification_date": "2018-04-30",
"name": "Update previously seen users in CloudTrail",
"original_authors": [
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
}
],
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/abnormally_high_instance_termination.json
+1 -1
View File
@@ -42,7 +42,7 @@
}
}
},
"eli5": "In this search, we query CloudTrail logs to look for events where an instance is successfully terminated by a particular user. Since we want to detect a high number of instances terminated within a short period, we create event buckets for 10-minute windows. We then calculate the total number of instances terminated by a particular user, as well as the average- and standard-deviation values. Assign a <code>threshold_value</code> in the search. Try starting with 3 (but it will likely need to be tweaked for your environment). The <code>eval</code> 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.",
"eli5": "In this search, we query CloudTrail logs to look for events where an instance is successfully terminated by a particular user. Since we want to detect a high number of instances terminated within a short period, we create event buckets for 10-minute windows. We then calculate the total number of instances terminated by a particular user, as well as the average- and standard-deviation values. Assign a `threshold_value` in the search. Try starting with 3 (but it will likely need to be tweaked for your environment). The `eval` function will set the outlier to 1 if the number of instances is greater than the average number of instances terminated, added to the multiplied value of threshold and standard deviation. We then filter out outliers with a value of 1 and show only those instance-termination events that happened within the previous 10 minutes.",
"entities": [
"userName"
],
detections/abnormally_high_instances_launched.json
+1 -1
View File
@@ -42,7 +42,7 @@
}
}
},
"eli5": "In this search, we query CloudTrail logs to look for events where an instance is successfully launched by a particular user. Since we want to detect a high number of instances launched within a short period, we create event buckets for 10-minute windows. We then calculate the total number of instances launched by a particular user, as well as the average and standard deviation values. Assign a <code>threshold_value</code> in the search. Start with 3 (but it will likely need to be tweaked for your environment). The <code>eval</code> 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.",
"eli5": "In this search, we query CloudTrail logs to look for events where an instance is successfully launched by a particular user. Since we want to detect a high number of instances launched within a short period, we create event buckets for 10-minute windows. We then calculate the total number of instances launched by a particular user, as well as the average and standard deviation values. Assign a `threshold_value` in the search. Start with 3 (but it will likely need to be tweaked for your environment). The `eval` function will set the outlier 1 if the number of instances is greater than the average number of instances terminated, added to the multiplied value of threshold and standard deviation. For your reference, we then keep only the outliers and calculate the number of standard deviations away the value is from the average.",
"entities": [
"userName"
],
detections/add_to_untrust_cert_store.json
+134 -134
View File
@@ -1,139 +1,139 @@
{
"asset_type": "Endpoint",
"confidence": "high",
"creation_date": "2018-04-09",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
"asset_type": "Endpoint",
"confidence": "high",
"creation_date": "2018-04-09",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "Attempt to add a certificate to the untrusted certificate store",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user, process_name",
"rule_description": "Attempt to add a certificate to the untrusted certificate store",
"rule_title": "Attempt To Add Certificate to Untrusted Store"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count min(_time) 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\")` | `ctime(firstTime)`|`ctime(lastTime)`",
"suppress": {
"suppress_fields": "process, dest",
"suppress_period": "86400s"
}
}
}
},
"eli5": "Attackers will often attempt to disable security tools in order to evade detection. It is also possible for end users to attempt to disable anti-virus or other security tools to circumvent restrictions they encounter while trying to execute other programs. One way malware may accomplish this is by adding the legitimate certificate used to sign the security software to the untrusted certificate store. This will cause the system to no longer trust the software signed with this certificate and disallow it from executing. This search simply looks for the execution of **certutil.exe** with the parameters `-addcert` and `disallowed`, which add a certification to the \"untrusted\" certificate store.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "6bc5243e-ef36-45dc-9b12-f4a6be131159",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "Attempt to add a certificate to the untrusted certificate store",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user, process_name",
"rule_description": "Attempt to add a certificate to the untrusted certificate store",
"rule_title": "Attempt To Add Certificate to Untrusted Store"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count min(_time) 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\")` | `ctime(firstTime)`|`ctime(lastTime)`",
"suppress": {
"suppress_fields": "process, dest",
"suppress_period": "86400s"
}
}
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"eli5": "Attackers will often attempt to disable security tools in order to evade detection. It is also possible for end users to attempt to disable anti-virus or other security tools to circumvent restrictions they encounter while trying to execute other programs. One way malware may accomplish this is by adding the legitimate certificate used to sign the security software to the untrusted certificate store. This will cause the system to no longer trust the software signed with this certificate and disallow it from executing. This search simply looks for the execution of <b>certutil.exe</b> with the parameters <code>-addcert</code> and <code>disallowed</code>, which add a certification to the \"untrusted\" certificate store.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "6bc5243e-ef36-45dc-9b12-f4a6be131159",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems.",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"mappings": {
"cis20": [
"CIS 3",
"CIS 5",
"CIS 8"
],
"kill_chain_phases": [
"Installation",
"Actions on Objectives"
],
"mitre_attack": [
"Defense Evasion",
"Disabling Security Tools"
],
"nist": [
"PR.PT",
"DE.CM",
"PR.IP"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"modification_date": "2018-11-15",
"name": "Attempt To Add Certificate To Untrusted Store",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems.",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"mappings": {
"cis20": [
"CIS 3",
"CIS 5",
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "4.0"
}
"kill_chain_phases": [
"Installation",
"Actions on Objectives"
],
"mitre_attack": [
"Defense Evasion",
"Disabling Security Tools"
],
"nist": [
"PR.PT",
"DE.CM",
"PR.IP"
]
},
"modification_date": "2018-11-15",
"name": "Attempt To Add Certificate To Untrusted Store",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "4.0"
}
detections/api_acitivity_from_previously_unseen_user_role.json
+2 -2
View File
@@ -20,7 +20,7 @@
"AWS"
]
},
"description": "This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is <code>AssumedRole</code>.",
"description": "This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`.",
"detect": {
"splunk": {
"correlation_rule": {
@@ -49,7 +49,7 @@
}
}
},
"eli5": "The subsearch will execute first and return the user roles and names of the API calls completed within the last hour, where the type of user identity is <code>AssumedRole</code>. It then appends the historical data to those results in the lookup file. Next, it recalculates the <code>earliest</code> and <code>latest</code> 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.",
"eli5": "The subsearch will execute first and return the user roles and names of the API calls completed within the last hour, where the type of user identity is `AssumedRole`. It then appends the historical data to those results in the lookup file. Next, it recalculates the `earliest` and `latest` fields for each user role, as well as the name of the API call, and returns only those roles and API calls that have first been seen in the past hour. This is combined with the main search to return the values of API calls, name of the user role, and the earliest and latest time of this activity. It is worth noting that the name of the role of a particular user is parsed as \"userName\" in the CloudTrail logs.",
"entities": [
"user"
],
detections/attrib_to_hide_files.json
+129 -129
View File
@@ -1,134 +1,134 @@
{
"asset_type": "",
"confidence": "medium",
"creation_date": "2017-10-23",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
"asset_type": "",
"confidence": "medium",
"creation_date": "2017-10-23",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user, process",
"rule_description": "Attrib.exe is often used by attackers to hide malware files and directories in windows environments. This rule detects command-line arguments used to hide a file/directory",
"rule_title": "Suspicious usage of attrib.exe on $dest$ "
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "30 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `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\")` | `ctime(firstTime)`|`ctime(lastTime)`",
"suppress": {
"suppress_fields": "dest, process",
"suppress_period": "86400s"
}
}
}
},
"eli5": "This search is looking to detect command-line execution with of attrib.exe binary with the +h flag set. The +h flag is used to hide a file.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "c77162d3-f93c-45cc-80c8-22f6b5264g9f",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user, process",
"rule_description": "Attrib.exe is often used by attackers to hide malware files and directories in windows environments. This rule detects command-line arguments used to hide a file/directory",
"rule_title": "Suspicious usage of attrib.exe on $dest$ "
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "30 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `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\")` | `ctime(firstTime)`|`ctime(lastTime)`",
"suppress": {
"suppress_fields": "dest, process",
"suppress_period": "86400s"
}
}
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"eli5": "This search is looking to detect command-line execution with of attrib.exe binary with the +h flag set. The +h flag is used to hide a file.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "c77162d3-f93c-45cc-80c8-22f6b5264g9f",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Some applications and users may legitimately use attrib.exe to interact with the files. ",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Defense Evasion",
"Persistence"
],
"nist": [
"DE.CM"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"modification_date": "2018-11-15",
"name": "Hiding Files And Directories With Attrib.exe",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Some applications and users may legitimately use attrib.exe to interact with the files. ",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Defense Evasion",
"Persistence"
],
"nist": [
"DE.CM"
]
},
"modification_date": "2018-11-15",
"name": "Hiding Files And Directories With Attrib.exe",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
detections/aws_activity_from_non_approved_accounts.json
+4 -4
View File
@@ -20,7 +20,7 @@
"AWS"
]
},
"description": "This search looks for successful CloudTrail activity by user accounts that are not listed in the identity table or <code>aws_service_accounts.csv</code>. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users.",
"description": "This search looks for successful CloudTrail activity by user accounts that are not listed in the identity table or `aws_service_accounts.csv`. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users.",
"detect": {
"splunk": {
"correlation_rule": {
@@ -49,11 +49,11 @@
}
}
},
"eli5": "In this search, we are looking for successful API calls via CloudTrail. We filter out events triggered by known users listed in the <code>identity_lookup_expanded</code> 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.",
"eli5": "In this search, we are looking for successful API calls via CloudTrail. We filter out events triggered by known users listed in the `identity_lookup_expanded` lookup file and the service accounts. Once filtered out, we output a table with the event names and count, as well as the first and last time a specific user or service is detected.",
"entities": [
"user"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also populate the <code>identity_lookup_expanded</code> 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.<br></br>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):<br></br><ul><li><b>Label:</b> AWS Event Name, <b>Field:</b> eventName</li><li><b>Label:</b> First Time, <b>Field:</b> firstTime</li><li><b>Label:</b> Last Time, <b>Field:</b> lastTime</li></ul><br></br>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`",
"how_to_implement": "You must install the AWS App for 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.\n\nThis 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):\n\n<ul><li>**Label:** AWS Event Name, **Field:** eventName</li><li>**Label:** First Time, **Field:** firstTime</li><li>**Label:** Last Time, **Field:** lastTime</li></ul>\n\nDetailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`",
"id": "ada0f478-84a8-4641-a3f1-d82362d4bd55",
"investigations": [
{
@@ -72,7 +72,7 @@
"type": "splunk"
}
],
"known_false_positives": "It's likely that you'll find activity detected by users/service accounts that are not listed in the <code>identity_lookup_expanded</code> or <code> aws_service_accounts.csv</code> file. If the user is a legitimate service account, update the <code>aws_service_accounts.csv</code> table with that entry.",
"known_false_positives": "It's likely that you'll find activity detected by users/service accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv` file. If the user is a legitimate service account, update the `aws_service_accounts.csv` table with that entry.",
"maintainers": [
{
"company": "Splunk",
detections/aws_activity_in_new_region.json
+1 -1
View File
@@ -49,7 +49,7 @@
}
}
},
"eli5": "In this search, we query CloudTrail logs to look for events that indicate that an instance was started in a particular region. Using the <code>previously_seen_aws_regions.csv</code> lookup file created using the support search, we compare the region where this instance was started to all previously observed regions. The <code>eval</code> and <code>if</code> 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 <code>previously_seen_aws_regions.csv</code>. Please maintain <code>previously_seen_aws_regions.csv</code>",
"eli5": "In this search, we query CloudTrail logs to look for events that indicate that an instance was started in a particular region. Using the `previously_seen_aws_regions.csv` lookup file created using the support search, we compare the region where this instance was started to all previously observed regions. The `eval` and `if` functions determine that the earliest times seen for this region and instance were within the last day. If a new region is detected, it will alert you with \"Instance Started in a New Region\". However, this region will be added to the list of `previously_seen_aws_regions.csv`. Please maintain `previously_seen_aws_regions.csv`",
"entities": [
"awsRegion"
],
detections/aws_cloud_provisioning_from_previously_unseen_city.json
+92 -92
View File
@@ -1,100 +1,100 @@
{
"asset_type": "AWS Instance",
"baselines": [
{
"id": "ac88e6a0-4fba-4dfd-b7b9-8964df7d1aee",
"name": "Previously Seen AWS Provisioning Activity Sources",
"type": "splunk"
}
"asset_type": "AWS Instance",
"baselines": [
{
"id": "ac88e6a0-4fba-4dfd-b7b9-8964df7d1aee",
"name": "Previously Seen AWS Provisioning Activity Sources",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2018-03-16",
"data_metadata": {
"data_source": [
"AWS CloudTrail"
],
"confidence": "medium",
"creation_date": "2018-03-16",
"data_metadata": {
"data_source": [
"AWS CloudTrail"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with \"Run\" or \"Create.\" ",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src_ip, city",
"rule_description": "Your AWS infrastructure was provisioned from a city, $city$, which has never before been seen provisioning your infrastructure.",
"rule_title": "AWS Provision Activity From $city$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* [search sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by City | eval newCity=if(firstTime >= relative_time(now(), \"-70m@m\"), 1, 0) | where newCity=1 | table City] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, City, eventName, errorCode",
"suppress": {
"suppress_fields": "dest",
"suppress_period": "14400s"
}
}
}
},
"eli5": "The subsearch returns all events with event names that start with \"Run\" or \"Create,\" and then does a <code>GeoIP</code> 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 <code>firstTime</code> and <code>lastTime</code> 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 <code>firstTime</code> and <code>lastTime</code> for each city. It returns only those events from cities that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action.",
"entities": [
"dest"
"data_sourcetypes": [
"aws:cloudtrail"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the \"Previously Seen AWS Provisioning Activity Sources\" support search once to create a history of previously seen locations that have provisioned AWS resources.",
"id": "344a1778-0b25-490c-adb1-de8beddf59cd",
"investigations": [
{
"id": "446ec87a-85c6-40d4-b060-bea4498281d6",
"name": "Get All AWS Activity From IP Address",
"type": "splunk"
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with \"Run\" or \"Create.\" ",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src_ip, city",
"rule_description": "Your AWS infrastructure was provisioned from a city, $city$, which has never before been seen provisioning your infrastructure.",
"rule_title": "AWS Provision Activity From $city$"
},
{
"id": "0abeeb40-1255-4b68-91d1-7a7eb410c4b8",
"name": "Get All AWS Activity From City",
"type": "splunk"
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* [search sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by City | eval newCity=if(firstTime >= relative_time(now(), \"-70m@m\"), 1, 0) | where newCity=1 | table City] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, City, eventName, errorCode",
"suppress": {
"suppress_fields": "dest",
"suppress_period": "14400s"
}
],
"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.<br/><br/> This search will fire any time a new city is seen in the <b>GeoIP</b> 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 <b>MaxMind GeoIP</b> that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 1"
],
"nist": [
"ID.AM"
]
}
}
},
"eli5": "The subsearch returns all events with event names that start with \"Run\" or \"Create,\" and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each city. It returns only those events from cities that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action.",
"entities": [
"dest"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the \"Previously Seen AWS Provisioning Activity Sources\" support search once to create a history of previously seen locations that have provisioned AWS resources.",
"id": "344a1778-0b25-490c-adb1-de8beddf59cd",
"investigations": [
{
"id": "446ec87a-85c6-40d4-b060-bea4498281d6",
"name": "Get All AWS Activity From IP Address",
"type": "splunk"
},
"modification_date": "2018-03-16",
"name": "AWS Cloud Provisioning From Previously Unseen City",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
{
"id": "0abeeb40-1255-4b68-91d1-7a7eb410c4b8",
"name": "Get All AWS Activity From City",
"type": "splunk"
}
],
"known_false_positives": "This is a strictly behavioral search, so we define \"false positive\" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no \"false positives\" in a traditional sense, there is definitely lots of noise.\n\n 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.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 1"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
"nist": [
"ID.AM"
]
},
"modification_date": "2018-03-16",
"name": "AWS Cloud Provisioning From Previously Unseen City",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/aws_cloud_provisioning_from_previously_unseen_country.json
+92 -92
View File
@@ -1,100 +1,100 @@
{
"asset_type": "AWS Instance",
"baselines": [
{
"id": "ac88e6a0-4fba-4dfd-b7b9-8964df7d1aee",
"name": "Previously Seen AWS Provisioning Activity Sources",
"type": "splunk"
}
"asset_type": "AWS Instance",
"baselines": [
{
"id": "ac88e6a0-4fba-4dfd-b7b9-8964df7d1aee",
"name": "Previously Seen AWS Provisioning Activity Sources",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2018-03-16",
"data_metadata": {
"data_source": [
"AWS CloudTrail"
],
"confidence": "medium",
"creation_date": "2018-03-16",
"data_metadata": {
"data_source": [
"AWS CloudTrail"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with \"Run\" or \"Create.\" ",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src_ip, country",
"rule_description": "Your AWS infrastructure was provisioned from a country, $country$, which has never before been seen provisioning your infrastructure.",
"rule_title": "AWS Provision Activity From $country$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* [search sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Country | eval newCountry=if(firstTime >= relative_time(now(), \"-70m@m\"), 1, 0) | where newCountry=1 | table Country] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Country, eventName, errorCode",
"suppress": {
"suppress_fields": "dest",
"suppress_period": "14400s"
}
}
}
},
"eli5": "The subsearch returns all events with event names that start with \"Run\" or \"Create,\" and then does a <code>GeoIP</code> 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 <code>firstTime</code> and <code>lastTime</code> 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 <code>firstTime</code> and <code>lastTime</code> for each country. It returns only those events from countries that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action.",
"entities": [
"dest"
"data_sourcetypes": [
"aws:cloudtrail"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the \"Previously Seen AWS Provisioning Activity Sources\" support search once to create a history of previously seen locations that have provisioned AWS resources.",
"id": "ceb8d3d8-06cb-49eb-beaf-829526e33ff0",
"investigations": [
{
"id": "446ec87a-85c6-40d4-b060-bea4498281d6",
"name": "Get All AWS Activity From IP Address",
"type": "splunk"
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with \"Run\" or \"Create.\" ",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src_ip, country",
"rule_description": "Your AWS infrastructure was provisioned from a country, $country$, which has never before been seen provisioning your infrastructure.",
"rule_title": "AWS Provision Activity From $country$"
},
{
"id": "e763cdb9-00da-41e0-9bda-444debc9501a",
"name": "Get All AWS Activity From Country",
"type": "splunk"
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* [search sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Country | eval newCountry=if(firstTime >= relative_time(now(), \"-70m@m\"), 1, 0) | where newCountry=1 | table Country] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Country, eventName, errorCode",
"suppress": {
"suppress_fields": "dest",
"suppress_period": "14400s"
}
],
"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.<br></br> This search will fire any time a new country is seen in the <b>GeoIP</b> 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 <b>MaxMind GeoIP</b> that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 1"
],
"nist": [
"ID.AM"
]
}
}
},
"eli5": "The subsearch returns all events with event names that start with \"Run\" or \"Create,\" and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each country. It returns only those events from countries that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action.",
"entities": [
"dest"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the \"Previously Seen AWS Provisioning Activity Sources\" support search once to create a history of previously seen locations that have provisioned AWS resources.",
"id": "ceb8d3d8-06cb-49eb-beaf-829526e33ff0",
"investigations": [
{
"id": "446ec87a-85c6-40d4-b060-bea4498281d6",
"name": "Get All AWS Activity From IP Address",
"type": "splunk"
},
"modification_date": "2018-03-16",
"name": "AWS Cloud Provisioning From Previously Unseen Country",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
{
"id": "e763cdb9-00da-41e0-9bda-444debc9501a",
"name": "Get All AWS Activity From Country",
"type": "splunk"
}
],
"known_false_positives": "This is a strictly behavioral search, so we define \"false positive\" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching 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.\n\n 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.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 1"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
"nist": [
"ID.AM"
]
},
"modification_date": "2018-03-16",
"name": "AWS Cloud Provisioning From Previously Unseen Country",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/aws_cloud_provisioning_from_previously_unseen_ip.json
+89 -89
View File
@@ -1,95 +1,95 @@
{
"asset_type": "AWS Instance",
"baselines": [
{
"id": "ac88e6a0-4fba-4dfd-b7b9-8964df7d1aee",
"name": "Previously Seen AWS Provisioning Activity Sources",
"type": "splunk"
}
"asset_type": "AWS Instance",
"baselines": [
{
"id": "ac88e6a0-4fba-4dfd-b7b9-8964df7d1aee",
"name": "Previously Seen AWS Provisioning Activity Sources",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2018-03-16",
"data_metadata": {
"data_source": [
"AWS CloudTrail"
],
"confidence": "medium",
"creation_date": "2018-03-16",
"data_metadata": {
"data_source": [
"AWS CloudTrail"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with \"Run\" or \"Create.\" ",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src_ip",
"rule_description": "Your AWS infrastructure was provisioned from an IP, $src_ip$, which has never before been seen provisioning your infrastructure.",
"rule_title": "AWS Provision Activity From $src_ip$"
},
"risk": {
"risk_object": "src_ip",
"risk_object_type": [
"system"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* [search sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress | eval newIP=if(firstTime >= relative_time(now(), \"-70m@m\"), 1, 0) | where newIP=1 | table sourceIPAddress] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, eventName, errorCode",
"suppress": {
"suppress_fields": "src_ip",
"suppress_period": "14400s"
}
}
}
},
"eli5": "The subsearch returns all events with event names that start with \"Run\" or \"Create,\" and then does a <code>GeoIP</code> 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 <code>firstTime</code> and <code>lastTime</code> 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 <code>firstTime</code> and <code>lastTime</code> for each city. It returns only those events from IP addresses that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action.",
"entities": [
"src_ip"
"data_sourcetypes": [
"aws:cloudtrail"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the \"Previously Seen AWS Provisioning Activity Sources\" support search once to create a history of previously seen locations that have provisioned AWS resources.",
"id": "42e15012-ac14-4801-94f4-f1acbe64880b",
"investigations": [
{
"id": "446ec87a-85c6-40d4-b060-bea4498281d6",
"name": "Get All AWS Activity From IP Address",
"type": "splunk"
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with \"Run\" or \"Create.\" ",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src_ip",
"rule_description": "Your AWS infrastructure was provisioned from an IP, $src_ip$, which has never before been seen provisioning your infrastructure.",
"rule_title": "AWS Provision Activity From $src_ip$"
},
"risk": {
"risk_object": "src_ip",
"risk_object_type": [
"system"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* [search sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress | eval newIP=if(firstTime >= relative_time(now(), \"-70m@m\"), 1, 0) | where newIP=1 | table sourceIPAddress] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, eventName, errorCode",
"suppress": {
"suppress_fields": "src_ip",
"suppress_period": "14400s"
}
}
}
},
"eli5": "The subsearch returns all events with event names that start with \"Run\" or \"Create,\" and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each city. It returns only those events from IP addresses that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action.",
"entities": [
"src_ip"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the \"Previously Seen AWS Provisioning Activity Sources\" support search once to create a history of previously seen locations that have provisioned AWS resources.",
"id": "42e15012-ac14-4801-94f4-f1acbe64880b",
"investigations": [
{
"id": "446ec87a-85c6-40d4-b060-bea4498281d6",
"name": "Get All AWS Activity From IP Address",
"type": "splunk"
}
],
"known_false_positives": "This is a strictly behavioral search, so we define \"false positive\" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no \"false positives\" in a traditional sense, there is definitely lots of noise.\n\n This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 1"
],
"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.<br/><br/> This search will fire any time a new IP address is seen in the <b>GeoIP</b> 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 <b>MaxMind GeoIP</b> that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 1"
],
"nist": [
"ID.AM"
]
},
"modification_date": "2018-03-16",
"name": "AWS Cloud Provisioning From Previously Unseen IP Address",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
"nist": [
"ID.AM"
]
},
"modification_date": "2018-03-16",
"name": "AWS Cloud Provisioning From Previously Unseen IP Address",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/aws_cloud_provisioning_from_previously_unseen_region.json
+92 -92
View File
@@ -1,100 +1,100 @@
{
"asset_type": "AWS Instance",
"baselines": [
{
"id": "ac88e6a0-4fba-4dfd-b7b9-8964df7d1aee",
"name": "Previously Seen AWS Provisioning Activity Sources",
"type": "splunk"
}
"asset_type": "AWS Instance",
"baselines": [
{
"id": "ac88e6a0-4fba-4dfd-b7b9-8964df7d1aee",
"name": "Previously Seen AWS Provisioning Activity Sources",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2018-03-16",
"data_metadata": {
"data_source": [
"AWS CloudTrail"
],
"confidence": "medium",
"creation_date": "2018-03-16",
"data_metadata": {
"data_source": [
"AWS CloudTrail"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with \"Run\" or \"Create.\"",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src_ip, Region",
"rule_description": "Your AWS infrastructure was provisioned from a region, $Region$, which has never before been seen provisioning your infrastructure.",
"rule_title": "AWS Provision Activity From $region$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* [search sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Region | eval newRegion=if(firstTime >= relative_time(now(), \"-70m@m\"), 1, 0) | where newRegion=1 | table Region] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Region, eventName, errorCode",
"suppress": {
"suppress_fields": "dest",
"suppress_period": "14400s"
}
}
}
},
"eli5": "The subsearch returns all events with event names that start with \"Run\" or \"Create,\" and then does a <code>GeoIP</code> 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 <code>firstTime</code> and <code>lastTime</code> 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 <code>firstTime</code> and <code>lastTime</code> for each city. It returns only those events from regions that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action.",
"entities": [
"dest"
"data_sourcetypes": [
"aws:cloudtrail"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the \"Previously Seen AWS Provisioning Activity Sources\" support search once to create a history of previously seen locations that have provisioned AWS resources.",
"id": "7971d3df-da82-4648-a6e5-b5637bea5253",
"investigations": [
{
"id": "446ec87a-85c6-40d4-b060-bea4498281d6",
"name": "Get All AWS Activity From IP Address",
"type": "splunk"
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with \"Run\" or \"Create.\"",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src_ip, Region",
"rule_description": "Your AWS infrastructure was provisioned from a region, $Region$, which has never before been seen provisioning your infrastructure.",
"rule_title": "AWS Provision Activity From $region$"
},
{
"id": "5b794bef-1743-4f6f-804a-43915a2702ff",
"name": "Get All AWS Activity From Region",
"type": "splunk"
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* [search sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Region | eval newRegion=if(firstTime >= relative_time(now(), \"-70m@m\"), 1, 0) | where newRegion=1 | table Region] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Region, eventName, errorCode",
"suppress": {
"suppress_fields": "dest",
"suppress_period": "14400s"
}
],
"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.<br/><br/> This search will fire any time a new region is seen in the <b>GeoIP</b> 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 <b>MaxMind GeoIP</b> that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 1"
],
"nist": [
"ID.AM"
]
}
}
},
"eli5": "The subsearch returns all events with event names that start with \"Run\" or \"Create,\" and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each city. It returns only those events from regions that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action.",
"entities": [
"dest"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the \"Previously Seen AWS Provisioning Activity Sources\" support search once to create a history of previously seen locations that have provisioned AWS resources.",
"id": "7971d3df-da82-4648-a6e5-b5637bea5253",
"investigations": [
{
"id": "446ec87a-85c6-40d4-b060-bea4498281d6",
"name": "Get All AWS Activity From IP Address",
"type": "splunk"
},
"modification_date": "2018-03-16",
"name": "AWS Cloud Provisioning From Previously Unseen Region",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
{
"id": "5b794bef-1743-4f6f-804a-43915a2702ff",
"name": "Get All AWS Activity From Region",
"type": "splunk"
}
],
"known_false_positives": "This is a strictly behavioral search, so we define \"false positive\" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no \"false positives\" in a traditional sense, there is definitely lots of noise.\n\n 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.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 1"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
"nist": [
"ID.AM"
]
},
"modification_date": "2018-03-16",
"name": "AWS Cloud Provisioning From Previously Unseen Region",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/aws_cross_account_activity_from_new_account.json
+105 -105
View File
@@ -1,113 +1,113 @@
{
"asset_type": "AWS Instance",
"baselines": [
{
"id": "1cc22b09-c867-416e-a511-cb36ac44aee2",
"name": "Previously Seen AWS Cross Account Activity",
"type": "splunk"
}
"asset_type": "AWS Instance",
"baselines": [
{
"id": "1cc22b09-c867-416e-a511-cb36ac44aee2",
"name": "Previously Seen AWS Cross Account Activity",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2018-02-01",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"confidence": "medium",
"creation_date": "2018-02-01",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for AssumeRole events where an IAM role in a different account is requested for the first time.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "requestingAccountId, requestedAccountId, src_user, dest_user",
"rule_description": "Access to $dest_user$ was requested for the first time by $src_user$",
"rule_title": "AWS Account $dest_user$ access by $src_user$"
},
"risk": {
"risk_object": "dest_user",
"risk_object_type": [
"user"
],
"risk_score": 20
},
"schedule": {
"cron_schedule": "5 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventName=AssumeRole | spath output=requestingAccountId path=userIdentity.accountId | spath output=requestedAccountId path=resources{}.accountId | search requestingAccountId=* | where requestingAccountId != requestedAccountId | inputlookup append=t previously_seen_aws_cross_account_activity | multireport [| stats min(eval(coalesce(firstTime, strptime(_time,\"%Y-%m-%d %H:%M:%S\")))) as firstTime max(eval(coalesce(strptime(_time,\"%Y-%m-%d %H:%M:%S\"), lastTime))) as lastTime by requestingAccountId, requestedAccountId | outputlookup previously_seen_aws_cross_account_activity | where fact=fiction] [| eventstats min(eval(coalesce(firstTime, strptime(_time,\"%Y-%m-%d %H:%M:%S\")))) as firstTime, max(eval(coalesce(strptime(_time,\"%Y-%m-%d %H:%M:%S\"), lastTime))) as lastTime by requestingAccountId, requestedAccountId | where firstTime >= relative_time(now(), \"-70m@m\") AND isnotnull(_time) | spath output=accessKeyId path=responseElements.credentials.accessKeyId | spath output=requestingARN path=resources{}.ARN | stats values(awsRegion) as awsRegion values(firstTime) as firstTime values(lastTime) as lastTime values(sharedEventID) as sharedEventID, values(requestingARN) as src_user, values(responseElements.assumedRoleUser.arn) as dest_user by _time, requestingAccountId, requestedAccountId, accessKeyId] | table _time, firstTime, lastTime, src_user, requestingAccountId, dest_user, requestedAccountId, awsRegion, accessKeyId, sharedEventID",
"suppress": {
"suppress_fields": "requestingAccountId, requestedAccountId",
"suppress_period": "14400s"
}
}
}
},
"eli5": "This search<ol><li>Retrieves the <b>AssumeRole</b> event</li><li>Verifies that the log entry contains a value for the account ID of the requesting account</li><li>Ensures that the requesting account ID does not match the account ID of the requested account</li><li>Pulls in the previously seen requesting and requested account IDs</li><li>Splits up and executes multiple search paths at the same.</li><li>The first path determines the <b>firstTime</b> and <b>lastTime</b> entries for the cache file</li><li>Outputs the data to the cache file.</li><li>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).</li></ol>The second pipeline adds the <b>firstTime</b> and <b>lastTime</b> 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 <code>isnotnull(_time)</code> will remove the entries from the cache file.<br></br>The search finishes by gathering the data that it will display to the user.",
"entities": [
"dest_user"
"data_sourcetypes": [
"aws:cloudtrail"
],
"how_to_implement": "You must install the AWS App for 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 <code>Previously Seen AWS Cross Account Activity</code> support search only once to create the baseline of previously seen cross account activity. Thanks to Pablo Vega at Recurly for suggesting improvements to the search.",
"id": "64fbbddf-fabf-4edf-80b3-0cc36ef37727",
"investigations": [
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for AssumeRole events where an IAM role in a different account is requested for the first time.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "requestingAccountId, requestedAccountId, src_user, dest_user",
"rule_description": "Access to $dest_user$ was requested for the first time by $src_user$",
"rule_title": "AWS Account $dest_user$ access by $src_user$"
},
{
"id": "703b65a4-a0ae-4171-965d-45507506c64f",
"name": "AWS Investigate User Activities By AccessKeyId",
"type": "splunk"
"risk": {
"risk_object": "dest_user",
"risk_object_type": [
"user"
],
"risk_score": 20
},
{
"id": "b0d2e6a8-75fa-4b1b-9486-3d32acadf891",
"name": "AWS Investigate User Activities By Source User",
"type": "splunk"
"schedule": {
"cron_schedule": "5 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventName=AssumeRole | spath output=requestingAccountId path=userIdentity.accountId | spath output=requestedAccountId path=resources{}.accountId | search requestingAccountId=* | where requestingAccountId != requestedAccountId | inputlookup append=t previously_seen_aws_cross_account_activity | multireport [| stats min(eval(coalesce(firstTime, strptime(_time,\"%Y-%m-%d %H:%M:%S\")))) as firstTime max(eval(coalesce(strptime(_time,\"%Y-%m-%d %H:%M:%S\"), lastTime))) as lastTime by requestingAccountId, requestedAccountId | outputlookup previously_seen_aws_cross_account_activity | where fact=fiction] [| eventstats min(eval(coalesce(firstTime, strptime(_time,\"%Y-%m-%d %H:%M:%S\")))) as firstTime, max(eval(coalesce(strptime(_time,\"%Y-%m-%d %H:%M:%S\"), lastTime))) as lastTime by requestingAccountId, requestedAccountId | where firstTime >= relative_time(now(), \"-70m@m\") AND isnotnull(_time) | spath output=accessKeyId path=responseElements.credentials.accessKeyId | spath output=requestingARN path=resources{}.ARN | stats values(awsRegion) as awsRegion values(firstTime) as firstTime values(lastTime) as lastTime values(sharedEventID) as sharedEventID, values(requestingARN) as src_user, values(responseElements.assumedRoleUser.arn) as dest_user by _time, requestingAccountId, requestedAccountId, accessKeyId] | table _time, firstTime, lastTime, src_user, requestingAccountId, dest_user, requestedAccountId, awsRegion, accessKeyId, sharedEventID",
"suppress": {
"suppress_fields": "requestingAccountId, requestedAccountId",
"suppress_period": "14400s"
}
],
"known_false_positives": "Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 16"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Credential Access"
],
"nist": [
"PR.AC",
"PR.DS",
"DE.AE"
]
}
}
},
"eli5": "This search\n\n1. Retrieves the **AssumeRole** event</li><li>Verifies that the log entry contains a value for the account ID of the requesting account</li><li>Ensures that the requesting account ID does not match the account ID of the requested account</li><li>Pulls in the previously seen requesting and requested account IDs</li><li>Splits up and executes multiple search paths at the same.</li><li>The first path determines the **firstTime** and **lastTime** entries for the cache file</li><li>Outputs the data to the cache file.</li><li>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).</li></ol>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.\n\nThe search finishes by gathering the data that it will display to the user.",
"entities": [
"dest_user"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the `Previously Seen AWS Cross Account Activity` support search only once to create the baseline of previously seen cross account activity. Thanks to Pablo Vega at Recurly for suggesting improvements to the search.",
"id": "64fbbddf-fabf-4edf-80b3-0cc36ef37727",
"investigations": [
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
"modification_date": "2018-11-02",
"name": "AWS Cross Account Activity From Previously Unseen Account",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
{
"id": "703b65a4-a0ae-4171-965d-45507506c64f",
"name": "AWS Investigate User Activities By AccessKeyId",
"type": "splunk"
},
{
"id": "b0d2e6a8-75fa-4b1b-9486-3d32acadf891",
"name": "AWS Investigate User Activities By Source User",
"type": "splunk"
}
],
"known_false_positives": "Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 16"
],
"references": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Credential Access"
],
"nist": [
"PR.AC",
"PR.DS",
"DE.AE"
]
},
"modification_date": "2018-11-02",
"name": "AWS Cross Account Activity From Previously Unseen Account",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"references": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
detections/aws_spike_acl_activity.json
+3 -3
View File
@@ -49,11 +49,11 @@
}
}
},
"eli5": "This search and its corresponding subsearch run through the following series of steps: <ol><li>Retrieve all the AWS CloudTrail log entries that have recorded AWS API calls specifically for creating/modifying/replacing network Access Control Lists (ACLs).</li><li>Kick off a subsearch that retrieves the same data and pulls out the ARN into a more friendly format.</li><li>Count the number of API calls per Amazon Resource Name (ARN).</li><li>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.</li><li>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. </li><li>Rename <code>apiCalls</code> as <code>latestCount</code>.</li><li>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.</li><li>Update the cache file with the latest results.</li><li>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.</li><li>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.</li><li>Filter out anything that it determines is not a spike and return the list of ARNs to the main search. </li></ol>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.",
"eli5": "This search and its corresponding subsearch run through the following series of steps: \n\n1. Retrieve all the AWS CloudTrail log entries that have recorded AWS API calls specifically for creating/modifying/replacing network Access Control Lists (ACLs).</li><li>Kick off a subsearch that retrieves the same data and pulls out the ARN into a more friendly format.</li><li>Count the number of API calls per Amazon Resource Name (ARN).</li><li>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.</li><li>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. </li><li>Rename `apiCalls` as `latestCount`.</li><li>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.</li><li>Update the cache file with the latest results.</li><li>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.</li><li>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.</li><li>Filter out anything that it determines is not a spike and return the list of ARNs to the main search. </li></ol>The main search subsequently gets the names of all the API calls, the number of unique API calls, and the total number of API calls for each of these ARNs. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average.",
"entities": [
"user"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify <code>dataPointThreshold</code> and <code>deviationThreshold</code> to better fit your environment. The <code>dataPointThreshold</code> variable is the minimum number of data points required to have a statistically significant amount of data to determine. The <code>deviationThreshold</code> 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 <code>NetworkACLEvents</code>.",
"how_to_implement": "You must install the AWS App for 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 `NetworkACLEvents`.",
"id": "ada0f478-84a8-4641-a1f1-e32372d4bd53",
"investigations": [
{
@@ -87,7 +87,7 @@
"type": "splunk"
}
],
"known_false_positives": "The false-positive rate may vary based on the values of<code>dataPointThreshold</code> and <code>deviationThreshold</code>. Please modify this according the your environment.",
"known_false_positives": "The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Please modify this according the your environment.",
"maintainers": [
{
"company": "Splunk",
detections/children_of_spoolsv.json
+132 -132
View File
@@ -1,137 +1,137 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2018-11-26",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2018-11-26",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, process_name, parent_process_name",
"rule_description": "A child process of spoolsv.exe was detected on $dest$.",
"rule_title": "Spoolsv.exe spawned a child process on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 60
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe AND Processes.process_name!=regsvr32.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `ctime(firstTime)` | `ctime(lastTime)`",
"suppress": {
"suppress_fields": "dest, parent_process_name",
"suppress_period": "86400s"
}
}
}
},
"eli5": "This search looks for child processes of spoolsv.exe, which is associated with the Print Spooler service on Windows. Children of this process typically run under the SYSTEM context. This search should address the POC developed for the Windows local-privilege-escalation exploit announced in September of 2018. The associated vulnerability was assigned CVE-2018-8440. More information is available at https://doublepulsar.com/task-scheduler-alpc-exploit-high-level-analysis-ff08cda6ad4f.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "aa0c4aeb-5b18-41c4-8c07-f1442d7599df",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, process_name, parent_process_name",
"rule_description": "A child process of spoolsv.exe was detected on $dest$.",
"rule_title": "Spoolsv.exe spawned a child process on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 60
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe AND Processes.process_name!=regsvr32.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `ctime(firstTime)` | `ctime(lastTime)`",
"suppress": {
"suppress_fields": "dest, parent_process_name",
"suppress_period": "86400s"
}
}
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"eli5": "This search looks for child processes of spoolsv.exe, which is associated with the Print Spooler service on Windows. Children of this process typically run under the SYSTEM context. This search should address the POC developed for the Windows local-privilege-escalation exploit announced in September of 2018. The associated vulnerability was assigned CVE-2018-8440. More information is available at https://doublepulsar.com/task-scheduler-alpc-exploit-high-level-analysis-ff08cda6ad4f.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "aa0c4aeb-5b18-41c4-8c07-f1442d7599df",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Some legitimate printer-related processes may show up as children of spoolsv.exe. You should confirm that any activity as legitimate and may be added as exclusions in the search.",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"mappings": {
"cis20": [
"CIS 5",
"CIS 8"
],
"kill_chain_phases": [
"Exploitation"
],
"mitre_attack": [
"Privilege Escalation",
"Exploitation for Privilege Escalation"
],
"nist": [
"PR.AC",
"PR.PT",
"DE.CM"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"modification_date": "2018-12-03",
"name": "Child Processes of Spoolsv.exe",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Some legitimate printer-related processes may show up as children of spoolsv.exe. You should confirm that any activity as legitimate and may be added as exclusions in the search.",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"mappings": {
"cis20": [
"CIS 5",
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
"kill_chain_phases": [
"Exploitation"
],
"mitre_attack": [
"Privilege Escalation",
"Exploitation for Privilege Escalation"
],
"nist": [
"PR.AC",
"PR.PT",
"DE.CM"
]
},
"modification_date": "2018-12-03",
"name": "Child Processes of Spoolsv.exe",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
detections/clients_connecting_to_multiple_dns_servers.json
+99 -99
View File
@@ -1,105 +1,105 @@
{
"asset_type": "Endpoint",
"baselines": [],
"confidence": "medium",
"creation_date": "2016-09-13",
"data_metadata": {
"data_models": [
"Network_Resolution"
],
"data_source": [
"DNS"
],
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src, dest",
"rule_description": "This search allows you to identify the endpoints that have connected to more than five DNS servers over the time frame specified in the search.",
"rule_title": "Client $src$ Connecting to Multiple DNS Servers"
},
"risk": {
"risk_object": "src",
"risk_object_type": [
"system"
],
"risk_score": 80
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count, values(DNS.dest) AS dest dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY by DNS.src | `drop_dm_object_name(\"Network_Resolution\")` |where dest_count > 5",
"suppress": {
"suppress_fields": "src",
"suppress_period": "86400s"
}
}
}
},
"eli5": "DNS Queries with multiple DNS servers from a single client is unusual and may be indicative of malicious activity. This search works by performing a count by the source of the distinct destinations for the DNS traffic. The search uses the <code>Network_Resolution</code> data model.",
"entities": [
"dest",
"src"
"asset_type": "Endpoint",
"baselines": [],
"confidence": "medium",
"creation_date": "2016-09-13",
"data_metadata": {
"data_models": [
"Network_Resolution"
],
"how_to_implement": "This search requires that DNS data is being ingested and populating the <code>Network_Resolution</code> 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.<br></br>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):<br></br><ul><li><b>Label:</b> Distinct DNS Connections, <b>Field:</b> dest_count</li></ul><br></br>Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`",
"id": "74ec6f18-604b-4202-a567-86b2066be3ce",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd72",
"name": "Get DNS Server History for a host",
"type": "splunk"
}
"data_source": [
"DNS"
],
"known_false_positives": "It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src, dest",
"rule_description": "This search allows you to identify the endpoints that have connected to more than five DNS servers over the time frame specified in the search.",
"rule_title": "Client $src$ Connecting to Multiple DNS Servers"
},
"risk": {
"risk_object": "src",
"risk_object_type": [
"system"
],
"risk_score": 80
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count, values(DNS.dest) AS dest dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY by DNS.src | `drop_dm_object_name(\"Network_Resolution\")` |where dest_count > 5",
"suppress": {
"suppress_fields": "src",
"suppress_period": "86400s"
}
}
}
},
"eli5": "DNS Queries with multiple DNS servers from a single client is unusual and may be indicative of malicious activity. This search works by performing a count by the source of the distinct destinations for the DNS traffic. The search uses the `Network_Resolution` data model.",
"entities": [
"dest",
"src"
],
"how_to_implement": "This search requires that DNS data is being ingested and populating the `Network_Resolution` data model. This data can come from DNS logs or from solutions that parse network traffic for this data, such as Splunk Stream or Bro.\n\nThis 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):\n\n<ul><li>**Label:** Distinct DNS Connections, **Field:** dest_count</li></ul>\n\nDetailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`",
"id": "74ec6f18-604b-4202-a567-86b2066be3ce",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd72",
"name": "Get DNS Server History for a host",
"type": "splunk"
}
],
"known_false_positives": "It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 9",
"CIS 12",
"CIS 13"
],
"mappings": {
"cis20": [
"CIS 9",
"CIS 12",
"CIS 13"
],
"kill_chain_phases": [
"Command and Control"
],
"mitre_attack": [
"Command and Control",
"Exfiltration",
"Exfiltration Over Alternative Protocol",
"Commonly Used Port",
"Standard Application Layer Protocol"
],
"nist": [
"PR.PT",
"DE.AE",
"PR.DS"
]
},
"modification_date": "2017-09-18",
"name": "Clients Connecting to Multiple DNS Servers",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"kill_chain_phases": [
"Command and Control"
],
"responses": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"mitre_attack": [
"Command and Control",
"Exfiltration",
"Exfiltration Over Alternative Protocol",
"Commonly Used Port",
"Standard Application Layer Protocol"
],
"nist": [
"PR.PT",
"DE.AE",
"PR.DS"
]
},
"modification_date": "2017-09-18",
"name": "Clients Connecting to Multiple DNS Servers",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"responses": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/common_ransomware_extensions.json
+134 -134
View File
@@ -1,140 +1,140 @@
{
"asset_type": "Endpoint",
"confidence": "high",
"creation_date": "2017-08-21",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon"
]
"asset_type": "Endpoint",
"confidence": "high",
"creation_date": "2017-08-21",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon"
]
},
"description": "The search looks for file modifications with extensions commonly used by Ransomware",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, file_name",
"rule_description": "A file modification was detected on $dest$ with an extension commonly used by ransomware.",
"rule_title": "Ransomware Extension detected on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 80
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `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)` | `ctime(lastTime)` | `ctime(firstTime)`| rex field=file_name \"(?<file_extension>\\.[^\\.]+)$\" | `ransomware_extensions`",
"suppress": {
"suppress_fields": "dest,file_name",
"suppress_period": "14400s"
}
}
}
},
"eli5": "This search looks at file modifications across your hosts and identifies files with extensions that are commonly associated with the encrypted files generated by ransomware.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.\n\nThis 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):\n\n<ul><li>**Label:** Name, **Field:** Name</li><li>**Label:** File Extension, **Field:** file_extension\n\nDetailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`",
"id": "a9e5c5db-db11-43ca-86a8-c852d1b2c0ec",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "The search looks for file modifications with extensions commonly used by Ransomware",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, file_name",
"rule_description": "A file modification was detected on $dest$ with an extension commonly used by ransomware.",
"rule_title": "Ransomware Extension detected on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 80
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `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)` | `ctime(lastTime)` | `ctime(firstTime)`| rex field=file_name \"(?<file_extension>\\.[^\\.]+)$\" | `ransomware_extensions`",
"suppress": {
"suppress_fields": "dest,file_name",
"suppress_period": "14400s"
}
}
}
{
"id": "d98675ed-da43-4a7e-96a7-eeca3232ba8e",
"name": "Get Update Logs For Endpoint",
"type": "splunk"
},
"eli5": "This search looks at file modifications across your hosts and identifies files with extensions that are commonly associated with the encrypted files generated by ransomware.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.<br></br>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):<br></br><ul><li><b>Label:</b> Name, <b>Field:</b> Name</li><li><b>Label:</b> File Extension, <b>Field:</b> file_extension<br></br>Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`",
"id": "a9e5c5db-db11-43ca-86a8-c852d1b2c0ec",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "d98675ed-da43-4a7e-96a7-eeca3232ba8e",
"name": "Get Update Logs For Endpoint",
"type": "splunk"
},
{
"id": "df7a7f50-30f2-4cde-8448-69d2d5f9b3c5",
"name": "Get Vulnerability Logs For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d44",
"name": "Get Backup Logs For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd22",
"name": "Investigate Web Activity From Host",
"type": "splunk"
}
],
"known_false_positives": "It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
},
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [],
"nist": [
"PR.PT",
"DE.CM"
]
{
"id": "df7a7f50-30f2-4cde-8448-69d2d5f9b3c5",
"name": "Get Vulnerability Logs For Endpoint",
"type": "splunk"
},
"modification_date": "2018-11-15",
"name": "Common Ransomware Extensions",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d44",
"name": "Get Backup Logs For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd22",
"name": "Investigate Web Activity From Host",
"type": "splunk"
}
],
"known_false_positives": "It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
},
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [],
"nist": [
"PR.PT",
"DE.CM"
]
},
"modification_date": "2018-11-15",
"name": "Common Ransomware Extensions",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
detections/create_hidden_shares.json
+130 -130
View File
@@ -1,136 +1,136 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2018-06-14",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2018-06-14",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search looks for the creation or deletion of hidden shares using net.exe.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest,process_name",
"rule_description": "Net.exe was used to create or delete hidden network shares by $user$ on $dest$",
"rule_title": "Hidden File shares created/deleted on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "5 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `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)` | `ctime(firstTime)`| `ctime(lastTime)` | search (process=*share* OR process=*delete*)| regex process=\"\\S+[$]\"",
"suppress": {
"suppress_fields": "dest,process_name",
"suppress_period": "86400s"
}
}
}
},
"eli5": "Net.exe is a built-in command-line tool on Windows that can be used to create, delete, and manage shared resources on the computer, both locally and remotely. Though this tool is used by Microsoft administrators to manage the network shares, attackers also leverage it to create and delete hidden file shares by appending \"$\" after the name of the share. To look for hidden shares, use a regular expression to look for a `(name_file_share)$`. 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 of hidden shares",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "qw9919ed-fe5f-492c-b139-151bb162140e",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "This search looks for the creation or deletion of hidden shares using net.exe.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest,process_name",
"rule_description": "Net.exe was used to create or delete hidden network shares by $user$ on $dest$",
"rule_title": "Hidden File shares created/deleted on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "5 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `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)` | `ctime(firstTime)`| `ctime(lastTime)` | search (process=*share* OR process=*delete*)| regex process=\"\\S+[$]\"",
"suppress": {
"suppress_fields": "dest,process_name",
"suppress_period": "86400s"
}
}
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"eli5": "Net.exe is a built-in command-line tool on Windows that can be used to create, delete, and manage shared resources on the computer, both locally and remotely. Though this tool is used by Microsoft administrators to manage the network shares, attackers also leverage it to create and delete hidden file shares by appending \"$\" after the name of the share. To look for hidden shares, use a regular expression to look for a <code>(name_file_share)$</code>. In this search, we are looking for the command-line execution of net.exe with command-line parameters such as <code>net</code>, <code>share</code>, or <code>delete</code> that may correspond to the creation of hidden shares",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "qw9919ed-fe5f-492c-b139-151bb162140e",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"Command-Line Interface",
"Persistence"
],
"nist": [
"PR.PT",
"DE.CM"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"modification_date": "2018-11-15",
"name": "Create or delete hidden shares using net.exe",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "3.0"
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"Command-Line Interface",
"Persistence"
],
"nist": [
"PR.PT",
"DE.CM"
]
},
"modification_date": "2018-11-15",
"name": "Create or delete hidden shares using net.exe",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "3.0"
}
detections/create_local_admin_via_net.json
+130 -130
View File
@@ -1,136 +1,136 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2018-03-28",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2018-03-28",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search looks for the creation of local administrator accounts using net.exe.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest",
"rule_description": "Net.exe was used to create local administrator accounts on $dest$.",
"rule_title": "Local administrator account created on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "0 8 * * *",
"earliest_time": "-1440m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `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)` | `ctime(firstTime)`| `ctime(lastTime)` | search (process=*localgroup* OR process=*/add* OR process=*user*)",
"suppress": {
"suppress_fields": "dest",
"suppress_period": "86400s"
}
}
}
},
"eli5": "Net.exe is a built-in Windows command-line tool that can be used to add, display, or modify user accounts. While Microsoft administrators use this tool to manage user groups, threat actors often leverage it to create local admin accounts to maintain persistence. In this search, we are looking for the execution of process net.exe with command-line parameters such as `localgroup`, `add`, or `user` that may correspond to the creation of local admin accounts or setting user/group properties.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "b89919ed-fe5f-492c-b139-151bb162040e",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "This search looks for the creation of local administrator accounts using net.exe.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest",
"rule_description": "Net.exe was used to create local administrator accounts on $dest$.",
"rule_title": "Local administrator account created on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "0 8 * * *",
"earliest_time": "-1440m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `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)` | `ctime(firstTime)`| `ctime(lastTime)` | search (process=*localgroup* OR process=*/add* OR process=*user*)",
"suppress": {
"suppress_fields": "dest",
"suppress_period": "86400s"
}
}
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"eli5": "Net.exe is a built-in Windows command-line tool that can be used to add, display, or modify user accounts. While Microsoft administrators use this tool to manage user groups, threat actors often leverage it to create local admin accounts to maintain persistence. In this search, we are looking for the execution of process net.exe with command-line parameters such as <code>localgroup</code>, <code>add</code>, or <code>user</code> that may correspond to the creation of local admin accounts or setting user/group properties.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "b89919ed-fe5f-492c-b139-151bb162040e",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Administrators often leverage net.exe to create admin accounts.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"Command-Line Interface",
"Persistence"
],
"nist": [
"PR.PT",
"DE.CM"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"modification_date": "2018-11-15",
"name": "Create local admin accounts using net.exe",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Administrators often leverage net.exe to create admin accounts.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"Command-Line Interface",
"Persistence"
],
"nist": [
"PR.PT",
"DE.CM"
]
},
"modification_date": "2018-11-15",
"name": "Create local admin accounts using net.exe",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
detections/dns_amplification_any_query.json
+102 -102
View File
@@ -1,110 +1,110 @@
{
"asset_type": "DNS Servers",
"confidence": "high",
"creation_date": "2016-08-24",
"data_metadata": {
"data_models": [
"Network_Resolution"
],
"data_source": [
"DNS"
],
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest",
"rule_description": "The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries.",
"rule_title": "Large Volume of DNS ANY Queries"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 60
},
"schedule": {
"cron_schedule": "*/5 * * * *",
"earliest_time": "-15m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count from datamodel=Network_Resolution where nodename=DNS \"DNS.message_type\"=\"QUERY\" \"DNS.record_type\"=\"ANY\" by \"DNS.dest\" | `drop_dm_object_name(\"DNS\")` | where count>200",
"suppress": {
"suppress_fields": "dest",
"suppress_period": "7200s"
}
}
}
},
"eli5": "This search counts the number of DNS ANY queries received in 5 minutes, and generates a Notable Event if the count exceeds a predefined threshold. The search returns the count, the first time, and the last time a DNS packet was observed with the ANY flag set.",
"entities": [
"dest"
"asset_type": "DNS Servers",
"confidence": "high",
"creation_date": "2016-08-24",
"data_metadata": {
"data_models": [
"Network_Resolution"
],
"how_to_implement": "To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model.",
"id": "8fa891f7-a533-4b3c-af85-5aa2e7c1f1eb",
"investigations": [
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
"data_source": [
"DNS"
],
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest",
"rule_description": "The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries.",
"rule_title": "Large Volume of DNS ANY Queries"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 60
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
"schedule": {
"cron_schedule": "*/5 * * * *",
"earliest_time": "-15m@m",
"latest_time": "-10m@m"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
"search": "| tstats `summariesonly` count from datamodel=Network_Resolution where nodename=DNS \"DNS.message_type\"=\"QUERY\" \"DNS.record_type\"=\"ANY\" by \"DNS.dest\" | `drop_dm_object_name(\"DNS\")` | where count>200",
"suppress": {
"suppress_fields": "dest",
"suppress_period": "7200s"
}
],
"known_false_positives": "Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 11",
"CIS 12"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"nist": [
"PR.PT",
"DE.AE",
"PR.IP"
]
}
}
},
"eli5": "This search counts the number of DNS ANY queries received in 5 minutes, and generates a Notable Event if the count exceeds a predefined threshold. The search returns the count, the first time, and the last time a DNS packet was observed with the ANY flag set.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model.",
"id": "8fa891f7-a533-4b3c-af85-5aa2e7c1f1eb",
"investigations": [
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
"modification_date": "2017-09-20",
"name": "Large Volume of DNS ANY Queries",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
}
],
"known_false_positives": "Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 11",
"CIS 12"
],
"references": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"kill_chain_phases": [
"Actions on Objectives"
],
"nist": [
"PR.PT",
"DE.AE",
"PR.IP"
]
},
"modification_date": "2017-09-20",
"name": "Large Volume of DNS ANY Queries",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"references": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/dns_evilginx_subdomains.json
+1 -1
View File
@@ -48,7 +48,7 @@
"entities": [
"src"
],
"how_to_implement": "You need to ingest data from your DNS logs in the Network_Resolution datamodel. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You will have to add legitimate domain names to the `legit_domains.csv` file shipped with the app. <br></br> <b>Splunk>Phantom Playbook Integration</b><br></br>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 <code>https://splunkbase.splunk.com/app/3411/</code>, 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. <br/>(Playbook link:<code>https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/</code>).<br></br>",
"how_to_implement": "You need to ingest data from your DNS logs in the Network_Resolution datamodel. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You will have to add legitimate domain names to the `legit_domains.csv` file shipped with the app. \n\n **Splunk>Phantom Playbook Integration**\n\nIf Splunk>Phantom is also configured in your environment, a Playbook called `Lets Encrypt Domain Investigate` can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the \"Phantom Instance\" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \n\n(Playbook link:`https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/`).\n\n",
"id": "24dd17b1-e2fb-4c31-878c-d4f226595bfa",
"investigations": [
{
detections/dns_query_length_mltk.json
+150 -150
View File
@@ -1,157 +1,157 @@
{
"asset_type": "Endpoint",
"baselines": [
{
"id": "c914844c-0ff5-4efc-8d44-c063443129ba",
"name": "Baseline of DNS Query Length - MLTK",
"type": "splunk"
}
"asset_type": "Endpoint",
"baselines": [
{
"id": "c914844c-0ff5-4efc-8d44-c063443129ba",
"name": "Baseline of DNS Query Length - MLTK",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2019-05-08",
"data_metadata": {
"data_models": [
"Network_Resolution"
],
"confidence": "medium",
"creation_date": "2019-05-08",
"data_metadata": {
"data_models": [
"Network_Resolution"
],
"data_source": [
"DNS"
],
"providing_technologies": [
"Splunk Stream",
"Bro"
]
"data_source": [
"DNS"
],
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src",
"rule_description": "Identify DNS traffic with unusual query lengths by record type",
"rule_title": "DNS query length outliers"
},
"risk": {
"risk_object": "src",
"risk_object_type": [
"system"
],
"risk_score": 40
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count min(_time) as start_time max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name(DNS)` | `ctime(firstTime)` | `ctime(lastTime)` | eval query_length = len(query) | apply dns_query_pdfmodel threshold=0.01 | rename \"IsOutlier(query_length)\" as isOutlier | search isOutlier > 0 | sort -query_length | table start_time end_time query record_type count src dest query_length",
"suppress": {
"suppress_fields": "query",
"suppress_period": "43200s"
}
}
}
},
"eli5": "Attackers often use random, long domain names for components of their attack infrastructure. This search leverages the probability distribution function algorithm provided by the Machine Learning Toolkit (MLTK) to identify outliers in the length of the DNS query for each record type observed. The companion search \"Baseline of DNS Query Length - MLTK\" creates a machine-learning (ML) model built over the historical data used by this search. The determination of what is considered an outlier may be adjusted via the threshold parameter in the search. More information on the algorithm used can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.",
"entities": [
"src"
],
"how_to_implement": "To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search \"Baseline of DNS Query Length - MLTK\" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\n\nThis 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):\n\n<ul><li>**Label:** DNS Query, **Field:** query</li><li>**Label:** DNS Query Length, **Field:** query_length</li><li>**Label:** Number of events, **Field:** count</li></ul>\n\nDetailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`",
"id": "85fbcfe8-9718-4911-adf6-7000d077a3a9",
"investigations": [
{
"id": "910e6512-edc9-4f93-ba24-5b786f47a672",
"name": "Get Process Responsible For The DNS Traffic",
"type": "splunk"
},
"description": "This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src",
"rule_description": "Identify DNS traffic with unusual query lengths by record type",
"rule_title": "DNS query length outliers"
},
"risk": {
"risk_object": "src",
"risk_object_type": [
"system"
],
"risk_score": 40
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count min(_time) as start_time max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name(DNS)` | `ctime(firstTime)` | `ctime(lastTime)` | eval query_length = len(query) | apply dns_query_pdfmodel threshold=0.01 | rename \"IsOutlier(query_length)\" as isOutlier | search isOutlier > 0 | sort -query_length | table start_time end_time query record_type count src dest query_length",
"suppress": {
"suppress_fields": "query",
"suppress_period": "43200s"
}
}
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"eli5": "Attackers often use random, long domain names for components of their attack infrastructure. This search leverages the probability distribution function algorithm provided by the Machine Learning Toolkit (MLTK) to identify outliers in the length of the DNS query for each record type observed. The companion search \"Baseline of DNS Query Length - MLTK\" creates a machine-learning (ML) model built over the historical data used by this search. The determination of what is considered an outlier may be adjusted via the threshold parameter in the search. More information on the algorithm used can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.",
"entities": [
"src"
],
"how_to_implement": "To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search \"Baseline of DNS Query Length - MLTK\" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.<br></br>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):<br></br><ul><li><b>Label:</b> DNS Query, <b>Field:</b> query</li><li><b>Label:</b> DNS Query Length, <b>Field:</b> query_length</li><li><b>Label:</b> Number of events, <b>Field:</b> count</li></ul><br></br>Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`",
"id": "85fbcfe8-9718-4911-adf6-7000d077a3a9",
"investigations": [
{
"id": "910e6512-edc9-4f93-ba24-5b786f47a672",
"name": "Get Process Responsible For The DNS Traffic",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd73",
"name": "Get DNS traffic ratio",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd72",
"name": "Get DNS Server History for a host",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "If you are seeing more results than desired, you may consider reducing the value for threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data.",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"mappings": {
"cis20": [
"CIS 8",
"CIS 12"
],
"kill_chain_phases": [
"Command and Control"
],
"mitre_attack": [
"Command and Control",
"Exfiltration",
"Commonly Used Port"
],
"nist": [
"PR.PT",
"DE.AE",
"DE.CM"
]
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd73",
"name": "Get DNS traffic ratio",
"type": "splunk"
},
"modification_date": "2019-05-08",
"name": "DNS Query Length Outliers - MLTK",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd72",
"name": "Get DNS Server History for a host",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "If you are seeing more results than desired, you may consider reducing the value for threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data.",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"mappings": {
"cis20": [
"CIS 8",
"CIS 12"
],
"references": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
"kill_chain_phases": [
"Command and Control"
],
"mitre_attack": [
"Command and Control",
"Exfiltration",
"Commonly Used Port"
],
"nist": [
"PR.PT",
"DE.AE",
"DE.CM"
]
},
"modification_date": "2019-05-08",
"name": "DNS Query Length Outliers - MLTK",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"references": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/dns_record_changed.json
+111 -111
View File
@@ -1,118 +1,118 @@
{
"asset_type": "Endpoint",
"baselines": [
{
"id": "c096f721-8842-42ce-bfc7-74bd8c72b7c3",
"name": "Discover DNS records",
"type": "splunk"
}
"asset_type": "Endpoint",
"baselines": [
{
"id": "c096f721-8842-42ce-bfc7-74bd8c72b7c3",
"name": "Discover DNS records",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2019-02-14",
"data_metadata": {
"data_models": [
"Network_Resolution"
],
"confidence": "medium",
"creation_date": "2019-02-14",
"data_metadata": {
"data_models": [
"Network_Resolution"
],
"data_source": [
"DNS"
],
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src",
"rule_description": "The table represents a list of DNS records and their responses for corporate domains that have recently changed",
"rule_title": "DNS record changed"
},
"risk": {
"risk_object": "src",
"risk_object_type": [
"system"
],
"risk_score": 40
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| inputlookup discovered_dns_records.csv | rename answer as discovered_answer | join domain[|tstats summariesonly=true count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!=\"unknown\" DNS.answer!=\"\" by DNS.query | rename DNS.query as query | where query!=\"unknown\" | rex field=query \"(?<domain>\\w+\\.\\w+?)(?:$|/)\"] | makemv delim=\" \" answer | makemv delim=\" \" type | sort -count | table count,src,domain,type,query,current_answer,discovered_answer | makemv current_answer | mvexpand current_answer | makemv discovered_answer | eval n=mvfind(discovered_answer, current_answer) | where isnull(n)",
"suppress": {
"suppress_fields": "src",
"suppress_period": "28800s"
}
}
}
},
"eli5": "Using a lookup `discover_dns_records` generated by support search \"Discover DNS records\" we check previous network traffic and make sure the responses have not changed.",
"entities": [
"src",
"dest"
"data_source": [
"DNS"
],
"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\". <br></br> <b>Splunk>Phantom Playbook Integration</b><br></br>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 <code>https://splunkbase.splunk.com/app/3411/</code>, 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. <br/>(Playbook Link:<code>https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/</code>).<br></br>",
"id": "44d3a43e-dcd5-49f7-8356-5209bb369065",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd72",
"name": "Get DNS Server History for a host",
"type": "splunk"
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src",
"rule_description": "The table represents a list of DNS records and their responses for corporate domains that have recently changed",
"rule_title": "DNS record changed"
},
{
"id": "c096f721-8842-42ce-bfc7-74bd9a72c712",
"name": "DNS Hijack Enrichment",
"type": "phantom"
"risk": {
"risk_object": "src",
"risk_object_type": [
"system"
],
"risk_score": 40
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| inputlookup discovered_dns_records.csv | rename answer as discovered_answer | join domain[|tstats summariesonly=true count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!=\"unknown\" DNS.answer!=\"\" by DNS.query | rename DNS.query as query | where query!=\"unknown\" | rex field=query \"(?<domain>\\w+\\.\\w+?)(?:$|/)\"] | makemv delim=\" \" answer | makemv delim=\" \" type | sort -count | table count,src,domain,type,query,current_answer,discovered_answer | makemv current_answer | mvexpand current_answer | makemv discovered_answer | eval n=mvfind(discovered_answer, current_answer) | where isnull(n)",
"suppress": {
"suppress_fields": "src",
"suppress_period": "28800s"
}
],
"known_false_positives": "Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate.",
"maintainers": [
{
"company": "Splunk",
"email": "jhernandez@splunk.com",
"name": "Jose Hernandez"
}
],
"mappings": {
"cis20": [
"CIS 1",
"CIS 3",
"CIS 8",
"CIS 12"
],
"kill_chain_phases": [
"Command and Control"
],
"mitre_attack": [
"Exfiltration",
"Command and Control",
"Defense Evasion",
"Commonly Used Port"
],
"nist": [
"ID.AM",
"PR.DS",
"PR.IP",
"DE.AE",
"DE.CM"
]
}
}
},
"eli5": "Using a lookup `discover_dns_records` generated by support search \"Discover DNS records\" we check previous network traffic and make sure the responses have not changed.",
"entities": [
"src",
"dest"
],
"how_to_implement": "To successfully implement this search you will need to ensure that DNS data is populating the `Network_Resolution` data model. It also requires that the `discover_dns_record` lookup table be populated by the included support search \"Discover DNS record\". \n\n **Splunk>Phantom Playbook Integration**\n\nIf 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. \n\n(Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\n\n",
"id": "44d3a43e-dcd5-49f7-8356-5209bb369065",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd72",
"name": "Get DNS Server History for a host",
"type": "splunk"
},
"modification_date": "2019-02-14",
"name": "DNS record changed",
"original_authors": [
{
"company": "Splunk",
"email": "jhernandez@splunk.com",
"name": "Jose Hernandez"
}
{
"id": "c096f721-8842-42ce-bfc7-74bd9a72c712",
"name": "DNS Hijack Enrichment",
"type": "phantom"
}
],
"known_false_positives": "Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate.",
"maintainers": [
{
"company": "Splunk",
"email": "jhernandez@splunk.com",
"name": "Jose Hernandez"
}
],
"mappings": {
"cis20": [
"CIS 1",
"CIS 3",
"CIS 8",
"CIS 12"
],
"responses": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"kill_chain_phases": [
"Command and Control"
],
"mitre_attack": [
"Exfiltration",
"Command and Control",
"Defense Evasion",
"Commonly Used Port"
],
"nist": [
"ID.AM",
"PR.DS",
"PR.IP",
"DE.AE",
"DE.CM"
]
},
"modification_date": "2019-02-14",
"name": "DNS record changed",
"original_authors": [
{
"company": "Splunk",
"email": "jhernandez@splunk.com",
"name": "Jose Hernandez"
}
],
"responses": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/dyn_dns_queries.json
+99 -99
View File
@@ -1,106 +1,106 @@
{
"asset_type": "Endpoint",
"baselines": [],
"confidence": "medium",
"creation_date": "2017-11-17",
"data_metadata": {
"data_models": [
"Network_Resolution"
],
"data_source": [
"DNS"
],
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, blacklists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "answer, src, query",
"rule_description": "The search has detected a host making outbound queries to Dynamic DNS providers",
"rule_title": "Host $src$ detected to make a query to a Dynamic DNS provider"
},
"risk": {
"risk_object": "src",
"risk_object_type": [
"system"
],
"risk_score": 20
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(DNS.answer) as answer min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name(\"DNS\")` | `ctime(firstTime)` | `dynamic_dns_providers`",
"suppress": {
"suppress_fields": "src, query",
"suppress_period": "86400s"
}
}
}
},
"eli5": "The search leverages an accelerated <code>Network_Resolution</code> 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 <code>dynamic_dns_providers</code> by each host (DNS.src).",
"entities": [
"dest",
"query"
"asset_type": "Endpoint",
"baselines": [],
"confidence": "medium",
"creation_date": "2017-11-17",
"data_metadata": {
"data_models": [
"Network_Resolution"
],
"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 <code>Network_Resolution</code> data model. This search also leverages a lookup file, <code>dynamic_dns_providers_default.csv</code>, 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 <code>dynamic_dns_providers_local.csv</code>.<br></br>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):<br></br><ul><li><b>Label:</b> DNS Query, <b>Field:</b> query</li><li><b>Label:</b> DNS Answer, <b>Field:</b> answer</li><li><b>Label:</b> IsDynamicDNS, <b>Field:</b> isDynDNS</li></ul><br></br>Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`",
"id": "c77162d3-f93c-45cc-80c8-22f6v5464g9f",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd72",
"name": "Get DNS Server History for a host",
"type": "splunk"
}
"data_source": [
"DNS"
],
"known_false_positives": "Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, blacklists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "answer, src, query",
"rule_description": "The search has detected a host making outbound queries to Dynamic DNS providers",
"rule_title": "Host $src$ detected to make a query to a Dynamic DNS provider"
},
"risk": {
"risk_object": "src",
"risk_object_type": [
"system"
],
"risk_score": 20
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(DNS.answer) as answer min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name(\"DNS\")` | `ctime(firstTime)` | `dynamic_dns_providers`",
"suppress": {
"suppress_fields": "src, query",
"suppress_period": "86400s"
}
}
}
},
"eli5": "The search leverages an accelerated `Network_Resolution` data model to count and list the values of resolved domains for each DNS query. It checks the results against the list of Dynamic DNS providers in the lookup `dynamic_dns_providers` by each host (DNS.src).",
"entities": [
"dest",
"query"
],
"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`.\n\nThis 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):\n\n<ul><li>**Label:** DNS Query, **Field:** query</li><li>**Label:** DNS Answer, **Field:** answer</li><li>**Label:** IsDynamicDNS, **Field:** isDynDNS</li></ul>\n\nDetailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`",
"id": "c77162d3-f93c-45cc-80c8-22f6v5464g9f",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd72",
"name": "Get DNS Server History for a host",
"type": "splunk"
}
],
"known_false_positives": "Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8",
"CIS 12",
"CIS 13"
],
"mappings": {
"cis20": [
"CIS 8",
"CIS 12",
"CIS 13"
],
"kill_chain_phases": [
"Command and Control",
"Actions on Objectives"
],
"mitre_attack": [
"Exfiltration",
"Exfiltration Over Command and Control Channel",
"Defense Evasion",
"Commonly Used Port"
],
"nist": [
"PR.DS",
"PR.PT",
"DE.AE",
"DE.CM"
]
},
"modification_date": "2017-09-18",
"name": "Detect hosts connecting to dynamic domain providers",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
"kill_chain_phases": [
"Command and Control",
"Actions on Objectives"
],
"responses": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
"mitre_attack": [
"Exfiltration",
"Exfiltration Over Command and Control Channel",
"Defense Evasion",
"Commonly Used Port"
],
"nist": [
"PR.DS",
"PR.PT",
"DE.AE",
"DE.CM"
]
},
"modification_date": "2017-09-18",
"name": "Detect hosts connecting to dynamic domain providers",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"responses": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/dyn_dns_web_traffic.json
+142 -142
View File
@@ -1,148 +1,148 @@
{
"asset_type": "Endpoint",
"confidence": "high",
"creation_date": "2018-09-06",
"data_metadata": {
"data_models": [
"Web"
],
"data_source": [
"Web Traffic"
],
"providing_technologies": [
"Splunk Stream",
"Bro",
"Bluecoat",
"Palo Alto Firewall"
]
"asset_type": "Endpoint",
"confidence": "high",
"creation_date": "2018-09-06",
"data_metadata": {
"data_models": [
"Web"
],
"data_source": [
"Web Traffic"
],
"providing_technologies": [
"Splunk Stream",
"Bro",
"Bluecoat",
"Palo Alto Firewall"
]
},
"description": "This search looks for web connections to dynamic DNS providers.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src, url, dest",
"rule_description": "The host $src$ has been detected making a web request to $url$, which is a listed as a dynamic DNS provider.",
"rule_title": "Dynamic DNS web traffic detected on $src$."
},
"risk": {
"risk_object": "src",
"risk_object_type": [
"system"
],
"risk_score": 40
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats summariesonly=true allow_old_summaries=true count values(Web.url) as url min(_time) as firstTime from datamodel=Web where Web.status=200 by Web.src Web.dest Web.status | `drop_dm_object_name(\"Web\")` | `ctime(firstTime)` | `dynamic_dns_web_traffic`",
"suppress": {
"suppress_fields": "src, url , dest",
"suppress_period": "86400s"
}
}
}
},
"eli5": "This search looks for hosts in your environment that may be communicating with a dynamic DNS provider. It checks each URL an endpoint is connecting to against a list of dynamic DNS providers. It returns the source and destination IP address of the web request, the URL requested, and the first time the event occurred.",
"entities": [
"src"
],
"how_to_implement": "This search requires you to be ingesting web-traffic logs. You can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis tool, such as Bro or Splunk Stream. The web data model must contain the URL being requested, the IP address of the host initiating the request, and the destination IP. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of dynamic DNS providers. Consider periodically updating this local lookup file with new domains.\n\nThis 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):\n\n<ul><li>**Label:** IsDynamicDNS, **Field:** isDynDNS</li></ul>\n\nDetailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`",
"id": "134da869-e264-4a8f-8d7e-fcd01c18f301",
"investigations": [
{
"id": "910e6512-edc9-4f93-ba24-5b786f47a672",
"name": "Get Process Responsible For The DNS Traffic",
"type": "splunk"
},
"description": "This search looks for web connections to dynamic DNS providers.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src, url, dest",
"rule_description": "The host $src$ has been detected making a web request to $url$, which is a listed as a dynamic DNS provider.",
"rule_title": "Dynamic DNS web traffic detected on $src$."
},
"risk": {
"risk_object": "src",
"risk_object_type": [
"system"
],
"risk_score": 40
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats summariesonly=true allow_old_summaries=true count values(Web.url) as url min(_time) as firstTime from datamodel=Web where Web.status=200 by Web.src Web.dest Web.status | `drop_dm_object_name(\"Web\")` | `ctime(firstTime)` | `dynamic_dns_web_traffic`",
"suppress": {
"suppress_fields": "src, url , dest",
"suppress_period": "86400s"
}
}
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"eli5": "This search looks for hosts in your environment that may be communicating with a dynamic DNS provider. It checks each URL an endpoint is connecting to against a list of dynamic DNS providers. It returns the source and destination IP address of the web request, the URL requested, and the first time the event occurred.",
"entities": [
"src"
],
"how_to_implement": "This search requires you to be ingesting web-traffic logs. You can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis tool, such as Bro or Splunk Stream. The web data model must contain the URL being requested, the IP address of the host initiating the request, and the destination IP. This search also leverages a lookup file, <code>dynamic_dns_providers_default.csv</code>, which contains a non-exhaustive list of dynamic DNS providers. Consider periodically updating this local lookup file with new domains.<br></br>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):<br></br><ul><li><b>Label:</b> IsDynamicDNS, <b>Field:</b> isDynDNS</li></ul><br></br>Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`",
"id": "134da869-e264-4a8f-8d7e-fcd01c18f301",
"investigations": [
{
"id": "910e6512-edc9-4f93-ba24-5b786f47a672",
"name": "Get Process Responsible For The DNS Traffic",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd73",
"name": "Get DNS traffic ratio",
"type": "splunk"
},
{
"id": "2f5b960b-71df-49c0-affc-74992ce60e45",
"name": "Investigate Web Activity From src_ip",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd72",
"name": "Get DNS Server History for a host",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "It is possible that list of dynamic DNS providers is outdated and/or that the URL being requested is legitimate.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 7",
"CIS 8"
],
"kill_chain_phases": [
"Command and Control",
"Actions on Objectives"
],
"mitre_attack": [
"Command and Control",
"Web Service",
"Exfiltration Over Command and Control Channel",
"Defense Evasion"
],
"nist": [
"PR.IP",
"DE.DP"
]
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd73",
"name": "Get DNS traffic ratio",
"type": "splunk"
},
"modification_date": "2018-09-06",
"name": "Detect web traffic to dynamic domain providers",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
{
"id": "2f5b960b-71df-49c0-affc-74992ce60e45",
"name": "Investigate Web Activity From src_ip",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd72",
"name": "Get DNS Server History for a host",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "It is possible that list of dynamic DNS providers is outdated and/or that the URL being requested is legitimate.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 7",
"CIS 8"
],
"references": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
"kill_chain_phases": [
"Command and Control",
"Actions on Objectives"
],
"mitre_attack": [
"Command and Control",
"Web Service",
"Exfiltration Over Command and Control Channel",
"Defense Evasion"
],
"nist": [
"PR.IP",
"DE.DP"
]
},
"modification_date": "2018-09-06",
"name": "Detect web traffic to dynamic domain providers",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"references": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/ec2_instance_created_by_previously_unseen_user.json
+118 -118
View File
@@ -1,125 +1,125 @@
{
"asset_type": "AWS Instance",
"baselines": [
{
"id": "6c767ac0-0906-4355-9a83-927f5ee7bdad",
"name": "Previously Seen EC2 Launches By User",
"type": "splunk"
}
"asset_type": "AWS Instance",
"baselines": [
{
"id": "6c767ac0-0906-4355-9a83-927f5ee7bdad",
"name": "Previously Seen EC2 Launches By User",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2018-03-15",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"confidence": "medium",
"creation_date": "2018-03-15",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for EC2 instances being created by users who have not created them before.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "user, dest",
"rule_description": "The EC2 instance $dest$ was created by $user$. This user has never created an EC2 instance before.",
"rule_title": "EC2 Instance Created By Previously Unseen User $user$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventName=RunInstances [search sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_launches_by_user.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_launches_by_user.csv | eval newUser=if(firstTime >= relative_time(now(), \"-70m@m\"), 1, 0) | where newUser=1 | `ctime(firstTime)` | `ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as user | table _time, user, dest, instanceType",
"suppress": {
"suppress_fields": "user, dest",
"suppress_period": "14400s"
}
}
}
},
"eli5": "The subsearch returns the ARNs of all successful EC2 instance launches within the last hour and then appends the historical data in the lookup file to those results. It then recalculates the `firstTime` and `lastTime` field for each ARN and returns only those ARNs that have first been seen in the past hour. This is combined with the main search to return the time, user, and instance id of those systems.",
"entities": [
"dest"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the \"Previously Seen EC2 Launches By User\" support search once to create a history of previously seen ARNs.",
"id": "22773e84-bac0-4595-b086-20d3f735b4f1",
"investigations": [
{
"id": "bc91a8cd-35e7-4bb2-6140-e756cc46fd71",
"name": "AWS Investigate User Activities By ARN",
"type": "splunk"
},
"description": "This search looks for EC2 instances being created by users who have not created them before.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "user, dest",
"rule_description": "The EC2 instance $dest$ was created by $user$. This user has never created an EC2 instance before.",
"rule_title": "EC2 Instance Created By Previously Unseen User $user$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventName=RunInstances [search sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_launches_by_user.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_launches_by_user.csv | eval newUser=if(firstTime >= relative_time(now(), \"-70m@m\"), 1, 0) | where newUser=1 | `ctime(firstTime)` | `ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as user | table _time, user, dest, instanceType",
"suppress": {
"suppress_fields": "user, dest",
"suppress_period": "14400s"
}
}
}
{
"id": "0e40fe83-3edb-4d86-8206-8fed36529ca6",
"name": "Get EC2 Launch Details",
"type": "splunk"
},
"eli5": "The subsearch returns the ARNs of all successful EC2 instance launches within the last hour and then appends the historical data in the lookup file to those results. It then recalculates the <code>firstTime</code> and <code>lastTime</code> field for each ARN and returns only those ARNs that have first been seen in the past hour. This is combined with the main search to return the time, user, and instance id of those systems.",
"entities": [
"dest"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the \"Previously Seen EC2 Launches By User\" support search once to create a history of previously seen ARNs.",
"id": "22773e84-bac0-4595-b086-20d3f735b4f1",
"investigations": [
{
"id": "bc91a8cd-35e7-4bb2-6140-e756cc46fd71",
"name": "AWS Investigate User Activities By ARN",
"type": "splunk"
},
{
"id": "0e40fe83-3edb-4d86-8206-8fed36529ca6",
"name": "Get EC2 Launch Details",
"type": "splunk"
},
{
"id": "f3db4d1b-5f33-4b01-c541-c7ah9514c242",
"name": "Get EC2 Instance Details by instanceId",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
},
{
"id": "bc91a8cd-35e7-4bb2-6140-e756cc46fd11",
"name": "Investigate AWS activities via region name",
"type": "splunk"
}
],
"known_false_positives": "It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 1"
],
"nist": [
"ID.AM"
]
{
"id": "f3db4d1b-5f33-4b01-c541-c7ah9514c242",
"name": "Get EC2 Instance Details by instanceId",
"type": "splunk"
},
"modification_date": "2018-03-12",
"name": "EC2 Instance Started With Previously Unseen User",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
},
{
"id": "bc91a8cd-35e7-4bb2-6140-e756cc46fd11",
"name": "Investigate AWS activities via region name",
"type": "splunk"
}
],
"known_false_positives": "It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 1"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
"nist": [
"ID.AM"
]
},
"modification_date": "2018-03-12",
"name": "EC2 Instance Started With Previously Unseen User",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/ec2_instance_modified_by_previously_unseen_user.json
+97 -97
View File
@@ -1,105 +1,105 @@
{
"asset_type": "AWS Instance",
"baselines": [
{
"id": "4d69091b-d975-4267-85df-888bd41034eb",
"name": "Previously Seen EC2 Modifications By User",
"type": "splunk"
}
"asset_type": "AWS Instance",
"baselines": [
{
"id": "4d69091b-d975-4267-85df-888bd41034eb",
"name": "Previously Seen EC2 Modifications By User",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2018-04-09",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"confidence": "medium",
"creation_date": "2018-04-09",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for EC2 instances being modified by users who have not previously modified them.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "user, dest",
"rule_description": "The EC2 instance $dest$ was modified by $user$. This user has never modified an EC2 instance before.",
"rule_title": "EC2 Instance Modified By Previously Unseen User $user$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail `ec2ModificationAPIs` [search sourcetype=aws:cloudtrail `ec2ModificationAPIs` errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_modifications_by_user | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user | eval newUser=if(firstTime >= relative_time(now(), \"-70m@m\"), 1, 0) | where newUser=1 | `ctime(firstTime)` | `ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=dest responseElements.instancesSet.items{}.instanceId | spath output=user userIdentity.arn | table _time, user, dest",
"suppress": {
"suppress_fields": "user, dest",
"suppress_period": "14400s"
}
}
}
},
"eli5": "The subsearch returns the ARNs of all successful EC2 instance modifications within the last hour and then appends the historical data in the lookup file to those results. EC2 modification APIs are defined by the macro <code>ec2ModificationAPIs</code>. The search then recalculates the <code>firstTime</code> and <code>lastTime</code> field for each ARN and returns only those ARNs that have first been seen in the past hour. This is combined with the main search to return the time, user, and instance ID of those systems.",
"entities": [
"dest"
"data_sourcetypes": [
"aws:cloudtrail"
],
"how_to_implement": "You must install the AWS App for 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 <code>ec2ModificationAPIs</code>.",
"id": "56f91724-cf3f-4666-84e1-e3712fb41e76",
"investigations": [
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for EC2 instances being modified by users who have not previously modified them.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "user, dest",
"rule_description": "The EC2 instance $dest$ was modified by $user$. This user has never modified an EC2 instance before.",
"rule_title": "EC2 Instance Modified By Previously Unseen User $user$"
},
{
"id": "f3db4d1b-5f33-4b01-c541-c7ah9514c242",
"name": "Get EC2 Instance Details by instanceId",
"type": "splunk"
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 30
},
{
"id": "bc91a8cd-35e7-4bb2-6140-e756cc46fd71",
"name": "AWS Investigate User Activities By ARN",
"type": "splunk"
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail `ec2ModificationAPIs` [search sourcetype=aws:cloudtrail `ec2ModificationAPIs` errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_modifications_by_user | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user | eval newUser=if(firstTime >= relative_time(now(), \"-70m@m\"), 1, 0) | where newUser=1 | `ctime(firstTime)` | `ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=dest responseElements.instancesSet.items{}.instanceId | spath output=user userIdentity.arn | table _time, user, dest",
"suppress": {
"suppress_fields": "user, dest",
"suppress_period": "14400s"
}
],
"known_false_positives": "It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 1"
],
"nist": [
"ID.AM"
]
}
}
},
"eli5": "The subsearch returns the ARNs of all successful EC2 instance modifications within the last hour and then appends the historical data in the lookup file to those results. EC2 modification APIs are defined by the macro `ec2ModificationAPIs`. The search then recalculates the `firstTime` and `lastTime` field for each ARN and returns only those ARNs that have first been seen in the past hour. This is combined with the main search to return the time, user, and instance ID of those systems.",
"entities": [
"dest"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the \"Previously Seen EC2 Launches By User\" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2ModificationAPIs`.",
"id": "56f91724-cf3f-4666-84e1-e3712fb41e76",
"investigations": [
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
"modification_date": "2018-04-09",
"name": "EC2 Instance Modified With Previously Unseen User",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
{
"id": "f3db4d1b-5f33-4b01-c541-c7ah9514c242",
"name": "Get EC2 Instance Details by instanceId",
"type": "splunk"
},
{
"id": "bc91a8cd-35e7-4bb2-6140-e756cc46fd71",
"name": "AWS Investigate User Activities By ARN",
"type": "splunk"
}
],
"known_false_positives": "It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 1"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
"nist": [
"ID.AM"
]
},
"modification_date": "2018-04-09",
"name": "EC2 Instance Modified With Previously Unseen User",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
detections/email_attachments_with_spaces.json
+87 -87
View File
@@ -1,93 +1,93 @@
{
"asset_type": "Endpoint",
"channel": "ESCU",
"confidence": "high",
"creation_date": "2017-04-21",
"data_metadata": {
"data_models": [
"Email"
],
"data_source": [
"Email"
],
"providing_technologies": [
"Microsoft Exchange"
]
},
"description": "Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src_user, file_name",
"rule_description": "The sender $src_user$ has sent an email with a suspicious amount of spaces in the file name: $file_name$",
"rule_title": "Suspicious Email Attachment from $src_user$"
},
"risk": {
"risk_object": "src_user",
"risk_object_type": [
"user"
],
"risk_score": 60
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name=\"*\" by All_Email.src_user, All_Email.file_name All_Email.message_id | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name(\"All_Email\")` | eval space_ratio = (mvcount(split(file_name,\" \"))-1)/len(file_name) | search space_ratio >= 0.1 | rex field=recipient_address \"(?<recipient_user>.*)@\"",
"suppress": {
"suppress_fields": "src_user",
"suppress_period": "86400s"
}
}
}
},
"eli5": "This search looks at any emails with file attachment names that contain many spaces, relative to the length of the file name. Specifically, it checks to see whether spaces make up more than 10% of the number of characters in the file name. This percentage can be tuned for each environment. The search will output the message ID of the email, the count, the sender and recipient addresses, the first and last time this event was seen, and the space ratio of the file attachment name.",
"entities": [
"src",
"message_id"
"asset_type": "Endpoint",
"channel": "ESCU",
"confidence": "high",
"creation_date": "2017-04-21",
"data_metadata": {
"data_models": [
"Email"
],
"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. <br></br> <b>Splunk Phantom Playbook Integration</b><br></br>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 <code>https://splunkbase.splunk.com/app/3411/</code> and add the correct hostname to the \"Phantom Instance\" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox.",
"id": "56e877a6-1455-4479-ada6-0550dc1e22f8",
"investigations": [
{
"id": "3096f721-8842-42ce-2fc7-742d8372b712",
"name": "Suspicious Email Attachment Investigate and Delete",
"type": "phantom"
}
"data_source": [
"Email"
],
"known_false_positives": "None at this time",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
"providing_technologies": [
"Microsoft Exchange"
]
},
"description": "Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src_user, file_name",
"rule_description": "The sender $src_user$ has sent an email with a suspicious amount of spaces in the file name: $file_name$",
"rule_title": "Suspicious Email Attachment from $src_user$"
},
"risk": {
"risk_object": "src_user",
"risk_object_type": [
"user"
],
"risk_score": 60
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name=\"*\" by All_Email.src_user, All_Email.file_name All_Email.message_id | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name(\"All_Email\")` | eval space_ratio = (mvcount(split(file_name,\" \"))-1)/len(file_name) | search space_ratio >= 0.1 | rex field=recipient_address \"(?<recipient_user>.*)@\"",
"suppress": {
"suppress_fields": "src_user",
"suppress_period": "86400s"
}
}
}
},
"eli5": "This search looks at any emails with file attachment names that contain many spaces, relative to the length of the file name. Specifically, it checks to see whether spaces make up more than 10% of the number of characters in the file name. This percentage can be tuned for each environment. The search will output the message ID of the email, the count, the sender and recipient addresses, the first and last time this event was seen, and the space ratio of the file attachment name.",
"entities": [
"src",
"message_id"
],
"how_to_implement": "You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. \n\n **Splunk Phantom Playbook Integration**\n\nIf Splunk Phantom is also configured in your environment, a playbook called \"Suspicious Email Attachment Investigate and Delete\" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the \"Phantom Instance\" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox.",
"id": "56e877a6-1455-4479-ada6-0550dc1e22f8",
"investigations": [
{
"id": "3096f721-8842-42ce-2fc7-742d8372b712",
"name": "Suspicious Email Attachment Investigate and Delete",
"type": "phantom"
}
],
"known_false_positives": "None at this time",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 7"
],
"mappings": {
"cis20": [
"CIS 7"
],
"kill_chain_phases": [
"Delivery"
],
"mitre_attack": [],
"nist": [
"PR.IP"
]
},
"modification_date": "2017-09-19",
"name": "Email Attachments With Lots Of Spaces",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
"kill_chain_phases": [
"Delivery"
],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
"mitre_attack": [],
"nist": [
"PR.IP"
]
},
"modification_date": "2017-09-19",
"name": "Email Attachments With Lots Of Spaces",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
detections/email_files_not_in_outlook_directory.json
+126 -126
View File
@@ -1,131 +1,131 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2017-12-13",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2017-12-13",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, file_path, action, file_name",
"rule_description": "The system $dest$ has email files outside of the normal Outlook directory ",
"rule_title": "Email files created or modified on $dest$ that are not in the normal Outlook directory"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.dll OR Filesystem.file_name=*.ost) Filesystem.file_path != \"C:\\\\Users\\\\*\\\\My Documents\\\\Outlook Files\\\\*\" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest | `drop_dm_object_name(\"Filesystem\")` | `ctime(firstTime)` | `ctime(lastTime)`",
"suppress": {
"suppress_fields": "dest, file_path",
"suppress_period": "86400s"
}
}
}
},
"eli5": "In this search, we are looking for activities consistent with an adversary collecting email data from local machines. The search will detect email files (files with .pst or .ost extensions) created in directories other than the standard Outlook directory (c:\\users\\username\\My Documents\\Outlook Files\\.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes.",
"id": "ee18ed37-0802-4268-9435-b3b91aaa18xx",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, file_path, action, file_name",
"rule_description": "The system $dest$ has email files outside of the normal Outlook directory ",
"rule_title": "Email files created or modified on $dest$ that are not in the normal Outlook directory"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.dll OR Filesystem.file_name=*.ost) Filesystem.file_path != \"C:\\\\Users\\\\*\\\\My Documents\\\\Outlook Files\\\\*\" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest | `drop_dm_object_name(\"Filesystem\")` | `ctime(firstTime)` | `ctime(lastTime)`",
"suppress": {
"suppress_fields": "dest, file_path",
"suppress_period": "86400s"
}
}
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"eli5": "In this search, we are looking for activities consistent with an adversary collecting email data from local machines. The search will detect email files (files with .pst or .ost extensions) created in directories other than the standard Outlook directory (c:\\users\\username\\My Documents\\Outlook Files\\.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes.",
"id": "ee18ed37-0802-4268-9435-b3b91aaa18xx",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Collection",
"Email Collection"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"modification_date": "2018-11-02",
"name": "Email files written outside of the Outlook directory",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Collection",
"Email Collection"
]
},
"modification_date": "2018-11-02",
"name": "Email files written outside of the Outlook directory",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/excessive_lockouts_from_endpoint.json
+89 -89
View File
@@ -1,97 +1,97 @@
{
"asset_type": "Windows",
"confidence": "low",
"creation_date": "2017-08-17",
"data_metadata": {
"data_models": [
"Change"
],
"data_source": [
"Windows Event Logs"
],
"providing_technologies": [
"Microsoft Windows"
]
},
"description": "This search identifies endpoints that have caused a relatively high number of account lockouts in a short period.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest",
"rule_description": "The system $dest$ has generated a high number of account lockouts.",
"rule_title": "$dest$ has generated a high number of account lockouts"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 40
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-4h@h",
"latest_time": "-5m@m"
},
"search": "| tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result=\"lockout\" by All_Changes.dest All_Changes.result |`drop_dm_object_name(\"All_Changes\")` |`drop_dm_object_name(\"Account_Management\")`| `ctime(firstTime)` | `ctime(lastTime)` | search count > 5",
"suppress": {
"suppress_fields": "dest",
"suppress_period": "86400s"
}
}
}
},
"eli5": "This search queries the `Change.All_Changes` datamodel under the nodename is `Account_Management` , where the result is \"lockout\", which indicates that an account has been locked out. It then counts the number of times an endpoint has caused an account lockout within a four hour window and displays those hosts with a count greater than or equal to five.",
"entities": [
"dest"
"asset_type": "Windows",
"confidence": "low",
"creation_date": "2017-08-17",
"data_metadata": {
"data_models": [
"Change"
],
"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. <br></br> <b>Splunk>Phantom Playbook Integration</b><br></br>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 <code>https://splunkbase.splunk.com/app/3411/</code>, 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. <br/>(Playbook Link:<code>https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/</code>).<br></br>",
"id": "c026e3dd-7e18-4abb-8f41-929e836efe74",
"investigations": [
{
"id": "ab62b5c1-95d4-4e71-8fd7-53a55db33da4",
"name": "Excessive Account Lockouts Enrichment And Response",
"type": "phantom"
}
"data_source": [
"Windows Event Logs"
],
"known_false_positives": "It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
"providing_technologies": [
"Microsoft Windows"
]
},
"description": "This search identifies endpoints that have caused a relatively high number of account lockouts in a short period.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest",
"rule_description": "The system $dest$ has generated a high number of account lockouts.",
"rule_title": "$dest$ has generated a high number of account lockouts"
},
{
"company": "Splunk",
"email": "proyer@splunk.com",
"name": "Philip Royer"
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 40
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-4h@h",
"latest_time": "-5m@m"
},
"search": "| tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result=\"lockout\" by All_Changes.dest All_Changes.result |`drop_dm_object_name(\"All_Changes\")` |`drop_dm_object_name(\"Account_Management\")`| `ctime(firstTime)` | `ctime(lastTime)` | search count > 5",
"suppress": {
"suppress_fields": "dest",
"suppress_period": "86400s"
}
],
"mappings": {
"cis20": [
"CIS 16"
],
"mitre_attack": [
"Initial Access",
"Valid Accounts"
],
"nist": [
"PR.IP"
]
}
}
},
"eli5": "This search queries the `Change.All_Changes` datamodel under the nodename is `Account_Management` , where the result is \"lockout\", which indicates that an account has been locked out. It then counts the number of times an endpoint has caused an account lockout within a four hour window and displays those hosts with a count greater than or equal to five.",
"entities": [
"dest"
],
"how_to_implement": "You must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. \n\n **Splunk>Phantom Playbook Integration**\n\nIf Splunk>Phantom is also configured in your environment, a Playbook called \"Excessive Account Lockouts Enrichment and Response\" can be configured to run when any results are found by this detection search. The Playbook executes the Contextual and Investigative searches in this Story, conducts additional information gathering on Windows endpoints, and takes a response action to shut down the affected endpoint. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the \"Phantom Instance\" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \n\n(Playbook Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`).\n\n",
"id": "c026e3dd-7e18-4abb-8f41-929e836efe74",
"investigations": [
{
"id": "ab62b5c1-95d4-4e71-8fd7-53a55db33da4",
"name": "Excessive Account Lockouts Enrichment And Response",
"type": "phantom"
}
],
"known_false_positives": "It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
},
"modification_date": "2019-04-18",
"name": "Detect Excessive Account Lockouts From Endpoint",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
{
"company": "Splunk",
"email": "proyer@splunk.com",
"name": "Philip Royer"
}
],
"mappings": {
"cis20": [
"CIS 16"
],
"responses": [],
"security_domain": "access",
"spec_version": 2,
"type": "splunk",
"version": "3.0"
"mitre_attack": [
"Initial Access",
"Valid Accounts"
],
"nist": [
"PR.IP"
]
},
"modification_date": "2019-04-18",
"name": "Detect Excessive Account Lockouts From Endpoint",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"responses": [],
"security_domain": "access",
"spec_version": 2,
"type": "splunk",
"version": "3.0"
}
detections/first_time_seen_cmd_line.json
+145 -145
View File
@@ -1,152 +1,152 @@
{
"asset_type": "Endpoint",
"baselines": [
{
"id": "fc0edc95-ff2b-48b0-9f6f-63da3789fd23",
"name": "Previously seen command line arguments",
"type": "splunk"
}
"asset_type": "Endpoint",
"baselines": [
{
"id": "fc0edc95-ff2b-48b0-9f6f-63da3789fd23",
"name": "Previously seen command line arguments",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2018-04-09",
"data_metadata": {
"data_models": [
"Endpoint"
],
"confidence": "medium",
"creation_date": "2018-04-09",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user, process",
"rule_description": "The system $dest$ executed a command-line argument, $process$, that has not previously been seen.",
"rule_title": "First-time seen command-line argument was detected on $dest$."
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "30 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = \"* /c *\" by Processes.process Processes.process_name Processes.parent_process_name Processes.dest| `drop_dm_object_name(Processes)`| `ctime(firstTime)` | `ctime(lastTime)` | search [| tstats `summariesonly` earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = \"* /c *\" by Processes.process | `drop_dm_object_name(Processes)` | inputlookup append=t previously_seen_cmd_line_arguments | stats min(firstTime) as firstTime, max(lastTime) as lastTime by process | outputlookup previously_seen_cmd_line_arguments | eval newCmdLineArgument=if(firstTime >= relative_time(now(), \"-70m@m\"), 1, 0) | where newCmdLineArgument=1 | `ctime(firstTime)` | `ctime(lastTime)` | table process]",
"suppress": {
"suppress_fields": "dest, process",
"suppress_period": "86400s"
}
}
}
},
"eli5": "The subsearch returns all events where `cmd.exe` was used with a `/c` parameter in the command-line arguments to execute other commands/programs. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for command-line execution and outputs this data to the lookup file to update the local cache. It returns only those events that have first been seen in the past one hour. This is combined with the main search to return the time, user, destination, process, parent process, and value of the command-line argument.",
"entities": [
"dest"
],
"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,\"&#151;which creates a lookup file called `previously_seen_cmd_line_arguments.csv`&#151;a historical baseline of all command-line arguments. You must also validate this list. For the search to do accurate calculation, ensure the search scheduling is the same value as the `relative_time` evaluation function.",
"id": "9be56c82-b1cc-4318-87eb-q138afaaqa39",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "This search looks for command-line arguments that use a <code>/c</code> parameter to execute a command that has not previously been seen.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user, process",
"rule_description": "The system $dest$ executed a command-line argument, $process$, that has not previously been seen.",
"rule_title": "First-time seen command-line argument was detected on $dest$."
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "30 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = \"* /c *\" by Processes.process Processes.process_name Processes.parent_process_name Processes.dest| `drop_dm_object_name(Processes)`| `ctime(firstTime)` | `ctime(lastTime)` | search [| tstats `summariesonly` earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = \"* /c *\" by Processes.process | `drop_dm_object_name(Processes)` | inputlookup append=t previously_seen_cmd_line_arguments | stats min(firstTime) as firstTime, max(lastTime) as lastTime by process | outputlookup previously_seen_cmd_line_arguments | eval newCmdLineArgument=if(firstTime >= relative_time(now(), \"-70m@m\"), 1, 0) | where newCmdLineArgument=1 | `ctime(firstTime)` | `ctime(lastTime)` | table process]",
"suppress": {
"suppress_fields": "dest, process",
"suppress_period": "86400s"
}
}
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"eli5": "The subsearch returns all events where <code>cmd.exe</code> was used with a <code>/c</code> 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 <code>firstTime</code> and <code>lastTime</code> field for command-line execution and outputs this data to the lookup file to update the local cache. It returns only those events that have first been seen in the past one hour. This is combined with the main search to return the time, user, destination, process, parent process, and value of the command-line argument.",
"entities": [
"dest"
],
"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,\"&#151;which creates a lookup file called <code>previously_seen_cmd_line_arguments.csv</code>&#151;a historical baseline of all command-line arguments. You must also validate this list. For the search to do accurate calculation, ensure the search scheduling is the same value as the `relative_time` evaluation function.",
"id": "9be56c82-b1cc-4318-87eb-q138afaaqa39",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd22",
"name": "Investigate Web Activity From Host",
"type": "splunk"
}
],
"known_false_positives": "Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 3",
"CIS 8"
],
"kill_chain_phases": [
"Command and Control",
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"Scripting",
"Persistence",
"Command-Line Interface"
],
"nist": [
"PR.PT",
"DE.CM",
"PR.IP"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"modification_date": "2019-03-04",
"name": "First time seen command line argument",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd22",
"name": "Investigate Web Activity From Host",
"type": "splunk"
}
],
"known_false_positives": "Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 3",
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "3.0"
"kill_chain_phases": [
"Command and Control",
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"Scripting",
"Persistence",
"Command-Line Interface"
],
"nist": [
"PR.PT",
"DE.CM",
"PR.IP"
]
},
"modification_date": "2019-03-04",
"name": "First time seen command line argument",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "3.0"
}
detections/first_time_seen_running_windows_service.json
+125 -125
View File
@@ -1,132 +1,132 @@
{
"asset_type": "Endpoint",
"baselines": [
{
"id": "64ce0ade-cb01-4678-bddd-d31c0b175394",
"name": "Previously Seen Running Windows Services",
"type": "splunk"
}
"asset_type": "Endpoint",
"baselines": [
{
"id": "64ce0ade-cb01-4678-bddd-d31c0b175394",
"name": "Previously Seen Running Windows Services",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2018-07-22",
"data_metadata": {
"data_eventtypes": [
"wineventlog_system"
],
"confidence": "medium",
"creation_date": "2018-07-22",
"data_metadata": {
"data_eventtypes": [
"wineventlog_system"
],
"data_source": [
"Windows Event Logs"
],
"providing_technologies": [
"Microsoft Windows"
]
"data_source": [
"Windows Event Logs"
],
"providing_technologies": [
"Microsoft Windows"
]
},
"description": "This search looks for the first time a Windows service is seen running in your environment.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "serviceName",
"rule_description": "The service $serviceName$ is running on $dest$. This is the first time this service has been run on any system.",
"rule_title": "First Time Seen Windows Service $serviceName$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 40
},
"schedule": {
"cron_schedule": "30 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "eventtype=wineventlog_system signature_id=7036 | rex field=Message \"The (?<serviceName>[\\w\\s-]*) service entered the (?<action>\\w*) state\" | where action=\"running\" | inputlookup append=t previously_seen_running_windows_services | multireport [| stats earliest(eval(coalesce(_time, firstTime))) as firstTime, latest(eval(coalesce(_time, lastTime))) as lastTime by serviceName | outputlookup previously_seen_running_windows_services | where fact=fiction] [| eventstats earliest(eval(coalesce(_time, firstTime))) as firstTime, latest(eval(coalesce(_time, lastTime))) as lastTime by serviceName | where firstTime >= relative_time(now(), \"-60m@m\") AND isnotnull(_time) | stats values(dest) as dest by _time, serviceName] | table _time, serviceName, dest",
"suppress": {
"suppress_fields": "serviceName, dest",
"suppress_period": "86400s"
}
}
}
},
"eli5": "This search looks for a change in the status of a Windows service and extracts the name of the service and the action taken by the service. Then the cache file of previously seen Windows services is added to the search. At this point, the search takes two different paths: the first updates the cache file with the latest information and the second searches for services that have never before been seen. It returns the time, the Windows host name, and the service name.",
"entities": [
"dest"
],
"how_to_implement": "While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs in order for this search to execute successfully. The support search, `Previously Seen Running Windows Services`, should be run before this search to create the baseline of known Windows services.",
"id": "823136f2-d755-4b6d-ae04-372b486a5808",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "This search looks for the first time a Windows service is seen running in your environment.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "serviceName",
"rule_description": "The service $serviceName$ is running on $dest$. This is the first time this service has been run on any system.",
"rule_title": "First Time Seen Windows Service $serviceName$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 40
},
"schedule": {
"cron_schedule": "30 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "eventtype=wineventlog_system signature_id=7036 | rex field=Message \"The (?<serviceName>[\\w\\s-]*) service entered the (?<action>\\w*) state\" | where action=\"running\" | inputlookup append=t previously_seen_running_windows_services | multireport [| stats earliest(eval(coalesce(_time, firstTime))) as firstTime, latest(eval(coalesce(_time, lastTime))) as lastTime by serviceName | outputlookup previously_seen_running_windows_services | where fact=fiction] [| eventstats earliest(eval(coalesce(_time, firstTime))) as firstTime, latest(eval(coalesce(_time, lastTime))) as lastTime by serviceName | where firstTime >= relative_time(now(), \"-60m@m\") AND isnotnull(_time) | stats values(dest) as dest by _time, serviceName] | table _time, serviceName, dest",
"suppress": {
"suppress_fields": "serviceName, dest",
"suppress_period": "86400s"
}
}
}
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"eli5": "This search looks for a change in the status of a Windows service and extracts the name of the service and the action taken by the service. Then the cache file of previously seen Windows services is added to the search. At this point, the search takes two different paths: the first updates the cache file with the latest information and the second searches for services that have never before been seen. It returns the time, the Windows host name, and the service name.",
"entities": [
"dest"
],
"how_to_implement": "While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs in order for this search to execute successfully. The support search, <code>Previously Seen Running Windows Services</code>, should be run before this search to create the baseline of known Windows services.",
"id": "823136f2-d755-4b6d-ae04-372b486a5808",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 2",
"CIS 9"
],
"kill_chain_phases": [
"Installation",
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"New Service"
],
"nist": [
"ID.AM",
"PR.DS",
"PR.AC",
"DE.AE"
]
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
"modification_date": "2019-02-27",
"name": "First Time Seen Running Windows Service",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 2",
"CIS 9"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
"kill_chain_phases": [
"Installation",
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"New Service"
],
"nist": [
"ID.AM",
"PR.DS",
"PR.AC",
"DE.AE"
]
},
"modification_date": "2019-02-27",
"name": "First Time Seen Running Windows Service",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/large_icmp_outbound.json
+122 -122
View File
@@ -1,128 +1,128 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2018-06-01",
"data_metadata": {
"data_models": [
"Network_Traffic"
],
"data_source": [
"Network Communications"
],
"providing_technologies": [
"Bro",
"Splunk Stream",
"Palo Alto Firewall"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2018-06-01",
"data_metadata": {
"data_models": [
"Network_Traffic"
],
"data_source": [
"Network Communications"
],
"providing_technologies": [
"Bro",
"Splunk Stream",
"Palo Alto Firewall"
]
},
"description": "This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src_ip, dest_ip",
"rule_description": "Large outbound ICMP packet detected.",
"rule_title": "Large ICMP packet from $src_ip$ to $dest_ip$ detected"
},
"risk": {
"risk_object": "src_ip",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count earliest(_time) as earliest latest(_time) as latest values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name(\"All_Traffic\")` | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | convert ctime(earliest) ctime(latest)",
"suppress": {
"suppress_fields": "src_ip",
"suppress_period": "28800s"
}
}
}
},
"eli5": "This search works by looking at fields in the Network_Traffic data model, which is populated by various firewalls and passive networking monitoring technologies. Specifically, the search looks for ICMP packets larger than 1,000 bytes with a destination that is external to your organization.",
"entities": [
"src_ip"
],
"how_to_implement": "In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in the `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model",
"id": "e9c102de-4d43-42a7-b1c8-8062ea297419",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src_ip, dest_ip",
"rule_description": "Large outbound ICMP packet detected.",
"rule_title": "Large ICMP packet from $src_ip$ to $dest_ip$ detected"
},
"risk": {
"risk_object": "src_ip",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count earliest(_time) as earliest latest(_time) as latest values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name(\"All_Traffic\")` | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | convert ctime(earliest) ctime(latest)",
"suppress": {
"suppress_fields": "src_ip",
"suppress_period": "28800s"
}
}
}
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"eli5": "This search works by looking at fields in the Network_Traffic data model, which is populated by various firewalls and passive networking monitoring technologies. Specifically, the search looks for ICMP packets larger than 1,000 bytes with a destination that is external to your organization.",
"entities": [
"src_ip"
],
"how_to_implement": "In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named <code>internal</code> to the CIDRs that host the company's assets in the <code>assets_by_cidr.csv</code> lookup file, which is located in <code>$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/</code>. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model",
"id": "e9c102de-4d43-42a7-b1c8-8062ea297419",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "ICMP packets are used in a variety of ways to help troubleshoot networking issues and ensure the proper flow of traffic. As such, it is possible that a large ICMP packet could be perfectly legitimate. If large ICMP packets are associated with command and control traffic, there will typically be a large number of these packets observed over time. If the search is providing a large number of false positives, you can modify the search to adjust the byte threshold or whitelist specific IP addresses, as necessary.",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"mappings": {
"cis20": [
"CIS 9",
"CIS 12"
],
"kill_chain_phases": [
"Command and Control"
],
"mitre_attack": [
"Command and Control",
"Standard Non-Application Layer Protocol"
],
"nist": [
"DE.AE"
]
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
"modification_date": "2018-06-01",
"name": "Detect Large Outbound ICMP Packets",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "ICMP packets are used in a variety of ways to help troubleshoot networking issues and ensure the proper flow of traffic. As such, it is possible that a large ICMP packet could be perfectly legitimate. If large ICMP packets are associated with command and control traffic, there will typically be a large number of these packets observed over time. If the search is providing a large number of false positives, you can modify the search to adjust the byte threshold or whitelist specific IP addresses, as necessary.",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"mappings": {
"cis20": [
"CIS 9",
"CIS 12"
],
"references": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
"kill_chain_phases": [
"Command and Control"
],
"mitre_attack": [
"Command and Control",
"Standard Non-Application Layer Protocol"
],
"nist": [
"DE.AE"
]
},
"modification_date": "2018-06-01",
"name": "Detect Large Outbound ICMP Packets",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"references": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
detections/local_admin_account_creation.json
+127 -127
View File
@@ -1,133 +1,133 @@
{
"asset_type": "Windows",
"confidence": "medium",
"creation_date": "2018-03-26",
"data_metadata": {
"data_eventtypes": [
"wineventlog_security"
],
"data_source": [
"Windows Event Logs"
],
"providing_technologies": [
"Microsoft Windows"
]
"asset_type": "Windows",
"confidence": "medium",
"creation_date": "2018-03-26",
"data_metadata": {
"data_eventtypes": [
"wineventlog_security"
],
"data_source": [
"Windows Event Logs"
],
"providing_technologies": [
"Microsoft Windows"
]
},
"description": "This search looks for newly created accounts that have been elevated to local administrators.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "user,src_user, dest",
"rule_description": "The new user account $user$ was created on $dest$ by $src_user$.",
"rule_title": "New local admin account $user$ created by $src_user$."
},
"risk": {
"risk_object": "user",
"risk_object_type": [
"system"
],
"risk_score": 40
},
"schedule": {
"cron_schedule": "0 9 * * *",
"earliest_time": "-1440m@m",
"latest_time": "-10m@m"
},
"search": "eventtype = wineventlog_security signature_id=4720 OR (signature_id=4732 Group_Name= Administrators) | transaction Security_ID maxspan=180m | search signature_id=4720 signature_id=4732 | table _time user dest signature_id Security_ID Group_Name src_user Message",
"suppress": {
"suppress_fields": "user",
"suppress_period": "86400s"
}
}
}
},
"eli5": "This search looks for Windows Event Code 4720 (account creation) and 4732 (account added to a security-enabled local group), where the group name is \"Administrators\", and determines whether they are generated for the same user's Security ID within three hours of each other. It will return the user account that was added, the Security ID, the group name to which the user was added, the account name of the user who initiated the action, and the subsequent message returned.",
"entities": [
"user"
],
"how_to_implement": "You must be ingesting Windows Security logs. You must also enable the account change auditing here:http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is \"Administrators\" to be able to look for the right group membership changes.\n\nThis 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):\n\n<ul><li>**Label:** Security ID, **Field:** Security_ID</li><li>**Label:** Group Name, **Field:** Group_Name</li><li>**Label:** Message, **Field:** Message</li></ul>\n\nDetailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`",
"id": "b25f6f62-0712-43c1-b203-083231ffd97d",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "This search looks for newly created accounts that have been elevated to local administrators.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "user,src_user, dest",
"rule_description": "The new user account $user$ was created on $dest$ by $src_user$.",
"rule_title": "New local admin account $user$ created by $src_user$."
},
"risk": {
"risk_object": "user",
"risk_object_type": [
"system"
],
"risk_score": 40
},
"schedule": {
"cron_schedule": "0 9 * * *",
"earliest_time": "-1440m@m",
"latest_time": "-10m@m"
},
"search": "eventtype = wineventlog_security signature_id=4720 OR (signature_id=4732 Group_Name= Administrators) | transaction Security_ID maxspan=180m | search signature_id=4720 signature_id=4732 | table _time user dest signature_id Security_ID Group_Name src_user Message",
"suppress": {
"suppress_fields": "user",
"suppress_period": "86400s"
}
}
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"eli5": "This search looks for Windows Event Code 4720 (account creation) and 4732 (account added to a security-enabled local group), where the group name is \"Administrators\", and determines whether they are generated for the same user's Security ID within three hours of each other. It will return the user account that was added, the Security ID, the group name to which the user was added, the account name of the user who initiated the action, and the subsequent message returned.",
"entities": [
"user"
],
"how_to_implement": "You must be ingesting Windows Security logs. You must also enable the account change auditing here:http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is \"Administrators\" to be able to look for the right group membership changes.<br></br>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):<br></br><ul><li><b>Label:</b> Security ID, <b>Field:</b> Security_ID</li><li><b>Label:</b> Group Name, <b>Field:</b> Group_Name</li><li><b>Label:</b> Message, <b>Field:</b> Message</li></ul><br></br>Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`",
"id": "b25f6f62-0712-43c1-b203-083231ffd97d",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not \"Administrators\", this search may generate an excessive number of false positives",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 16"
],
"kill_chain_phases": [
"Actions on Objectives",
"Command and Control"
],
"mitre_attack": [
"Valid Accounts",
"Defense Evasion",
"Persistence"
],
"nist": [
"PR.AC",
"DE.CM"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"modification_date": "2019-02-28",
"name": "Detect New Local Admin account",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not \"Administrators\", this search may generate an excessive number of false positives",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 16"
],
"references": [],
"security_domain": "access",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
"kill_chain_phases": [
"Actions on Objectives",
"Command and Control"
],
"mitre_attack": [
"Valid Accounts",
"Defense Evasion",
"Persistence"
],
"nist": [
"PR.AC",
"DE.CM"
]
},
"modification_date": "2019-02-28",
"name": "Detect New Local Admin account",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"references": [],
"security_domain": "access",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/malicious_application_shimming_via_registry.json
+133 -133
View File
@@ -1,138 +1,138 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2017-08-27",
"data_metadata": {
"data_models": [
"Change_Analysis"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2017-08-27",
"data_metadata": {
"data_models": [
"Change_Analysis"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon"
]
},
"description": "This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user",
"rule_description": "A registry key that is used for persistence on Windows was modified on $dest$ by $user$",
"rule_title": "Registry Key Associated With SHIM databases on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Change_Analysis.All_Changes where All_Changes.object_category=registry AND (All_Changes.object_path=\"*CurrentVersion\\\\AppCompatFlags\\\\Custom*\" OR All_Changes.object_path=\"*CurrentVersion\\\\AppCompatFlags\\\\InstalledSDB*\") by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object, All_Changes.object_path | `drop_dm_object_name(\"All_Changes\")`",
"suppress": {
"suppress_fields": "dest,object_path",
"suppress_period": "86400s"
}
}
}
},
"eli5": "In this search, we look for modifications to registry keys used for shim databases on Microsoft platforms via the object_category and object_path field in the Change_Analysis data model and give you the destination, command used to initiate the change, the user who conducted this activity, the resource affected(object), and the whole path of the object. An application compatibility shim is a small library that transparently intercepts an API (via hooking), changes the parameters passed, handles the operation itself, or redirects the operation elsewhere, such as additional code stored on a system. This capability can be also leveraged by attackers to create and store malicious files in a shim database as observed in CARBANAK backdoor.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must populate the Change_Analysis data model. This is typically populated via endpoint detection and response products, such as Carbon Black or other endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.",
"id": "f5f6af30-7aa7-4295-bfe9-07fe87c01bbb",
"investigations": [
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41xf9",
"name": "Get Registry Activities",
"type": "splunk"
},
"description": "This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user",
"rule_description": "A registry key that is used for persistence on Windows was modified on $dest$ by $user$",
"rule_title": "Registry Key Associated With SHIM databases on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Change_Analysis.All_Changes where All_Changes.object_category=registry AND (All_Changes.object_path=\"*CurrentVersion\\\\AppCompatFlags\\\\Custom*\" OR All_Changes.object_path=\"*CurrentVersion\\\\AppCompatFlags\\\\InstalledSDB*\") by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object, All_Changes.object_path | `drop_dm_object_name(\"All_Changes\")`",
"suppress": {
"suppress_fields": "dest,object_path",
"suppress_period": "86400s"
}
}
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"eli5": "In this search, we look for modifications to registry keys used for shim databases on Microsoft platforms via the object_category and object_path field in the Change_Analysis data model and give you the destination, command used to initiate the change, the user who conducted this activity, the resource affected(object), and the whole path of the object. An application compatibility shim is a small library that transparently intercepts an API (via hooking), changes the parameters passed, handles the operation itself, or redirects the operation elsewhere, such as additional code stored on a system. This capability can be also leveraged by attackers to create and store malicious files in a shim database as observed in CARBANAK backdoor.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must populate the Change_Analysis data model. This is typically populated via endpoint detection and response products, such as Carbon Black or other endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.",
"id": "f5f6af30-7aa7-4295-bfe9-07fe87c01bbb",
"investigations": [
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41xf9",
"name": "Get Registry Activities",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Persistence",
"Application Shimming"
],
"nist": [
"PR.PT",
"DE.CM"
]
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"modification_date": "2017-09-15",
"name": "Registry Keys for Creating SHIM Databases",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Persistence",
"Application Shimming"
],
"nist": [
"PR.PT",
"DE.CM"
]
},
"modification_date": "2017-09-15",
"name": "Registry Keys for Creating SHIM Databases",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/malicious_powershell_process_execpolicy.json
+134 -134
View File
@@ -1,140 +1,140 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2018-03-19",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2018-03-19",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user, process_name",
"rule_description": "The system $dest$ executed a PowerShell process with parameters to bypass the local execution policy.",
"rule_title": "PowerShell process with -executionpolicy bypass detected on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "50 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)`| search process=* -ex* OR process=* bypass *",
"suppress": {
"suppress_fields": "dest, process_name",
"suppress_period": "14400s"
}
}
}
},
"eli5": "This search looks for PowerShell processes that were launched using a parameter designed to bypass the local PowerShell execution policy. By default, the policy is set to \"Restricted,\" which disables the execution of PowerShell scripts. In environments that make heavy use of PowerShell, the policy can be set to allow only scripts signed by a trusted publisher. Malicious PowerShell use almost always includes the parameter `-ExecutionPolicy bypass`. PowerShell is very liberal when it comes to interpreting command-line parameters passed to it. For example, the parameter we look for, `-ExecutionPolicy`, can be abbreviated to `-Execution`, `-Exec`, or even `-ex`. As such, we look for `* -ex*`, which should catch all variations of this parameter, followed by the keyword `bypass`. This search will return the host, the user the process ran under, the process and its command-line arguments, the number of times it has seen this process, and the first and last times it saw this process.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "9be56c82-b1cc-4318-87eb-d138afaaca39",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user, process_name",
"rule_description": "The system $dest$ executed a PowerShell process with parameters to bypass the local execution policy.",
"rule_title": "PowerShell process with -executionpolicy bypass detected on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "50 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)`| search process=* -ex* OR process=* bypass *",
"suppress": {
"suppress_fields": "dest, process_name",
"suppress_period": "14400s"
}
}
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"eli5": "This search looks for PowerShell processes that were launched using a parameter designed to bypass the local PowerShell execution policy. By default, the policy is set to \"Restricted,\" which disables the execution of PowerShell scripts. In environments that make heavy use of PowerShell, the policy can be set to allow only scripts signed by a trusted publisher. Malicious PowerShell use almost always includes the parameter <code>-ExecutionPolicy bypass</code>. PowerShell is very liberal when it comes to interpreting command-line parameters passed to it. For example, the parameter we look for, <code>-ExecutionPolicy</code>, can be abbreviated to <code>-Execution</code>, <code>-Exec</code>, or even <code>-ex</code>. As such, we look for <code>* -ex*</code>, which should catch all variations of this parameter, followed by the keyword <code>bypass</code>. This search will return the host, the user the process ran under, the process and its command-line arguments, the number of times it has seen this process, and the first and last times it saw this process.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "9be56c82-b1cc-4318-87eb-d138afaaca39",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "There may be legitimate reasons to bypass the PowerShell execution policy. The PowerShell script being run with this parameter should be validated to ensure that it is legitimate.",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"mappings": {
"cis20": [
"CIS 3",
"CIS 7",
"CIS 8"
],
"kill_chain_phases": [
"Command and Control",
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"PowerShell",
"Scripting"
],
"nist": [
"PR.PT",
"DE.CM",
"PR.IP"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"modification_date": "2018-12-03",
"name": "Malicious PowerShell Process - Execution Policy Bypass",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "There may be legitimate reasons to bypass the PowerShell execution policy. The PowerShell script being run with this parameter should be validated to ensure that it is legitimate.",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"mappings": {
"cis20": [
"CIS 3",
"CIS 7",
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "3.0"
"kill_chain_phases": [
"Command and Control",
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"PowerShell",
"Scripting"
],
"nist": [
"PR.PT",
"DE.CM",
"PR.IP"
]
},
"modification_date": "2018-12-03",
"name": "Malicious PowerShell Process - Execution Policy Bypass",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "3.0"
}
detections/malicious_powershell_process_multiple_suspicious_arguments.json
+130 -130
View File
@@ -1,135 +1,135 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2016-09-18",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2016-09-18",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user, process, process_name",
"rule_description": "The system $dest$ executed a PowerShell that had an encoded command on the command-line, attempted to bypass local execution policy, and prevented the display of an interactive prompt to the user.",
"rule_title": "PowerShell process with multiple suspicious command-line arguments detected on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 60
},
"schedule": {
"cron_schedule": "50 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)`| search (process=*-EncodedCommand* OR process=*-enc*) process=*-Exec* AND process=*-NonI*",
"suppress": {
"suppress_fields": "dest, process_name",
"suppress_period": "14400s"
}
}
}
},
"eli5": "This search looks for PowerShell processes that have a number of suspicious flags on the command-line. It is looking for flags are passing encoded commands on the command-line. The flags `-EncodedCommand` and `-enc` are two different possible flags that can be used to pass base64 encoded commands to PowerShell. The `*-Exec*` flag looks to see it the default execution policy of PowerShell is being overridden, while the `*-NonI*` flag tells the PowerShell process that this will be a noninteractive process, so the user doesn't know about the process. This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "2cdb91d2-542c-497f-b252-be495e71f38c",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user, process, process_name",
"rule_description": "The system $dest$ executed a PowerShell that had an encoded command on the command-line, attempted to bypass local execution policy, and prevented the display of an interactive prompt to the user.",
"rule_title": "PowerShell process with multiple suspicious command-line arguments detected on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 60
},
"schedule": {
"cron_schedule": "50 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)`| search (process=*-EncodedCommand* OR process=*-enc*) process=*-Exec* AND process=*-NonI*",
"suppress": {
"suppress_fields": "dest, process_name",
"suppress_period": "14400s"
}
}
}
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"eli5": "This search looks for PowerShell processes that have a number of suspicious flags on the command-line. It is looking for flags are passing encoded commands on the command-line. The flags <code>-EncodedCommand</code> and <code>-enc</code> are two different possible flags that can be used to pass base64 encoded commands to PowerShell. The <code>*-Exec*</code> flag looks to see it the default execution policy of PowerShell is being overridden, while the <code>*-NonI*</code> flag tells the PowerShell process that this will be a noninteractive process, so the user doesn't know about the process. This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "2cdb91d2-542c-497f-b252-be495e71f38c",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Legitimate process can have this combination of command-line options, but it's not common.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 3",
"CIS 7",
"CIS 8"
],
"kill_chain_phases": [
"Command and Control",
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"PowerShell",
"Scripting"
],
"nist": [
"PR.PT",
"DE.CM",
"PR.IP"
]
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
"modification_date": "2018-12-03",
"name": "Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Legitimate process can have this combination of command-line options, but it's not common.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 3",
"CIS 7",
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "3.0"
}
"kill_chain_phases": [
"Command and Control",
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"PowerShell",
"Scripting"
],
"nist": [
"PR.PT",
"DE.CM",
"PR.IP"
]
},
"modification_date": "2018-12-03",
"name": "Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "3.0"
}
detections/malicious_powershell_process_obfuscation_techniques.json
+130 -130
View File
@@ -1,135 +1,135 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2017-04-25",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2017-04-25",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user, process_name, process",
"rule_description": "The system $dest$ executed a PowerShell process that has evidence of obfuscation on the command-line",
"rule_title": "PowerShell process with an obfuscation techniques detected on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 60
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)`| eval num_obfuscation = (mvcount(split(process, \"`\"))-1) + (mvcount(split(process, \"^\"))-1) | search num_obfuscation > 0",
"suppress": {
"suppress_fields": "dest,process_name,process",
"suppress_period": "14400s"
}
}
}
},
"eli5": "This search looks for PowerShell processes that are passing command-line arguments with unusual characters (backticks and carets) that are PowerShell specific escape characters. Attackers use this obfuscation technique since it does not affect the functionality of PowerShell and it will bypass standard security controls that look for straight up malicious strings and commands. The search counts the occurrence of these obfuscation characters and lists out destination IPs running these PowerShell commands.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "cde75cf6-3c7a-4dd6-af01-27cdb4511fd4",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user, process_name, process",
"rule_description": "The system $dest$ executed a PowerShell process that has evidence of obfuscation on the command-line",
"rule_title": "PowerShell process with an obfuscation techniques detected on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 60
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)`| eval num_obfuscation = (mvcount(split(process, \"`\"))-1) + (mvcount(split(process, \"^\"))-1) | search num_obfuscation > 0",
"suppress": {
"suppress_fields": "dest,process_name,process",
"suppress_period": "14400s"
}
}
}
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"eli5": "This search looks for PowerShell processes that are passing command-line arguments with unusual characters (backticks and carets) that are PowerShell specific escape characters. Attackers use this obfuscation technique since it does not affect the functionality of PowerShell and it will bypass standard security controls that look for straight up malicious strings and commands. The search counts the occurrence of these obfuscation characters and lists out destination IPs running these PowerShell commands.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "cde75cf6-3c7a-4dd6-af01-27cdb4511fd4",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "These characters might be legitimately on the command-line, but it is not common.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 3",
"CIS 7",
"CIS 8"
],
"kill_chain_phases": [
"Command and Control",
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"PowerShell",
"Scripting"
],
"nist": [
"PR.PT",
"DE.CM",
"PR.IP"
]
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
"modification_date": "2018-12-03",
"name": "Malicious PowerShell Process With Obfuscation Techniques",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "These characters might be legitimately on the command-line, but it is not common.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 3",
"CIS 7",
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
"kill_chain_phases": [
"Command and Control",
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"PowerShell",
"Scripting"
],
"nist": [
"PR.PT",
"DE.CM",
"PR.IP"
]
},
"modification_date": "2018-12-03",
"name": "Malicious PowerShell Process With Obfuscation Techniques",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
detections/netbackup_failed_backup.json
+109 -109
View File
@@ -1,115 +1,115 @@
{
"asset_type": "Endpoint",
"baselines": [
{
"id": "b2178fed-592f-492b-b851-74161678aa56",
"name": "Monitor Unsuccessful Backups",
"type": "splunk"
},
{
"id": "b4d0dfb2-2195-4f6e-93a3-48468ed9734e",
"name": "Monitor Successful Backups",
"type": "splunk"
}
],
"confidence": "high",
"creation_date": "2017-06-15",
"data_metadata": {
"data_source": [
"Backup Systems"
],
"data_sourcetypes": [
"netbackup_logs"
],
"providing_technologies": [
"Netbackup"
]
"asset_type": "Endpoint",
"baselines": [
{
"id": "b2178fed-592f-492b-b851-74161678aa56",
"name": "Monitor Unsuccessful Backups",
"type": "splunk"
},
"description": "This search gives you the hosts where a backup was attempted and then failed.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest",
"rule_description": "The system $dest$ attempted a backup but encountered an error.",
"rule_title": "Failed backup attempt by $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 10
},
"schedule": {
"cron_schedule": "0 7 * * *",
"earliest_time": "-24h@h",
"latest_time": "-10m@m"
},
"search": "sourcetype=\"netbackup_logs\" | stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE | search MESSAGE=\"An error occurred, failed to backup.\" | `ctime(latestTime)` | rename COMPUTERNAME as dest, MESSAGE as signature | table latestTime, dest, signature",
"suppress": {
"suppress_fields": "dest",
"suppress_period": "86400s"
}
}
{
"id": "b4d0dfb2-2195-4f6e-93a3-48468ed9734e",
"name": "Monitor Successful Backups",
"type": "splunk"
}
],
"confidence": "high",
"creation_date": "2017-06-15",
"data_metadata": {
"data_source": [
"Backup Systems"
],
"data_sourcetypes": [
"netbackup_logs"
],
"providing_technologies": [
"Netbackup"
]
},
"description": "This search gives you the hosts where a backup was attempted and then failed.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest",
"rule_description": "The system $dest$ attempted a backup but encountered an error.",
"rule_title": "Failed backup attempt by $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 10
},
"schedule": {
"cron_schedule": "0 7 * * *",
"earliest_time": "-24h@h",
"latest_time": "-10m@m"
},
"search": "sourcetype=\"netbackup_logs\" | stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE | search MESSAGE=\"An error occurred, failed to backup.\" | `ctime(latestTime)` | rename COMPUTERNAME as dest, MESSAGE as signature | table latestTime, dest, signature",
"suppress": {
"suppress_fields": "dest",
"suppress_period": "86400s"
}
}
}
},
"eli5": "This search looks across the most recent backup events for each host, and returns those messages that indicate there was a backup failure.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution.",
"id": "a34aae96-ccf8-4aaa-952c-3ea21444444f",
"investigations": [
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
"eli5": "This search looks across the most recent backup events for each host, and returns those messages that indicate there was a backup failure.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution.",
"id": "a34aae96-ccf8-4aaa-952c-3ea21444444f",
"investigations": [
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-aaaa-4bb2-8140-e756cc06fd72",
"name": "All backup logs for host",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
}
],
"known_false_positives": "None identified",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 10"
],
"nist": [
"PR.IP"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
"modification_date": "2017-09-12",
"name": "Unsuccessful Netbackup backups",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
{
"id": "bc91a8cf-aaaa-4bb2-8140-e756cc06fd72",
"name": "All backup logs for host",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
}
],
"known_false_positives": "None identified",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 10"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"nist": [
"PR.IP"
]
},
"modification_date": "2017-09-12",
"name": "Unsuccessful Netbackup backups",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/netsh_launching_process.json
+131 -131
View File
@@ -1,136 +1,136 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2018-01-04",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2018-01-04",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, process, parent_process",
"rule_description": "A process, $process$, is spawned by netsh.exe. It is highly unlikely for netsh to have any child processes.",
"rule_title": "Process spawned by netsh.exe detected on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process=\"C:\\Windows\\System32\\netsh.exe\" by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name(\"Processes\")` | `ctime(firstTime)`|`ctime(lastTime)`",
"suppress": {
"suppress_fields": "dest, process",
"suppress_period": "86400s"
}
}
}
},
"eli5": "This search looks for all processes with the parent process \"c:\\Windows\\System32\\netsh.exe\" and returns the process, the command line used to execute it, the host name, and the user context under which it ran.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must be ingesting logs with the process name, command-line arguments, and parent processes from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.",
"id": "b89919ed-fe5f-492c-b139-95dbb162041e",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, process, parent_process",
"rule_description": "A process, $process$, is spawned by netsh.exe. It is highly unlikely for netsh to have any child processes.",
"rule_title": "Process spawned by netsh.exe detected on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process=\"C:\\Windows\\System32\\netsh.exe\" by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name(\"Processes\")` | `ctime(firstTime)`|`ctime(lastTime)`",
"suppress": {
"suppress_fields": "dest, process",
"suppress_period": "86400s"
}
}
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"eli5": "This search looks for all processes with the parent process \"c:\\Windows\\System32\\netsh.exe\" and returns the process, the command line used to execute it, the host name, and the user context under which it ran.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must be ingesting logs with the process name, command-line arguments, and parent processes from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.",
"id": "b89919ed-fe5f-492c-b139-95dbb162041e",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd22",
"name": "Investigate Web Activity From Host",
"type": "splunk"
}
],
"known_false_positives": "It is unusual for netsh.exe to have any child processes in most environments. It makes sense to investigate the child process and verify whether the process spawned is legitimate.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"Command-Line Interface",
"Persistence"
],
"nist": [
"PR.PT",
"DE.CM"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"modification_date": "2018-11-02",
"name": "Processes created by netsh",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd22",
"name": "Investigate Web Activity From Host",
"type": "splunk"
}
],
"known_false_positives": "It is unusual for netsh.exe to have any child processes in most environments. It makes sense to investigate the child process and verify whether the process spawned is legitimate.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"Command-Line Interface",
"Persistence"
],
"nist": [
"PR.PT",
"DE.CM"
]
},
"modification_date": "2018-11-02",
"name": "Processes created by netsh",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
detections/new_aws_console_login_by_user.json
+105 -105
View File
@@ -1,111 +1,111 @@
{
"asset_type": "AWS Instance",
"baselines": [
{
"id": "fc0edc95-ff2b-48b0-9f6f-63da3789fd03",
"name": "Previously seen users in CloudTrail",
"type": "splunk"
"asset_type": "AWS Instance",
"baselines": [
{
"id": "fc0edc95-ff2b-48b0-9f6f-63da3789fd03",
"name": "Previously seen users in CloudTrail",
"type": "splunk"
},
{
"id": "06c036e6-d6d7-4daa-bd76-411c3d356031",
"name": "Update previously seen users in CloudTrail",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2018-02-26",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "user",
"rule_description": "A new user has logged into the AWS console",
"rule_title": "AWS Console Login by New User"
},
{
"id": "06c036e6-d6d7-4daa-bd76-411c3d356031",
"name": "Update previously seen users in CloudTrail",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2018-02-26",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "user",
"rule_description": "A new user has logged into the AWS console",
"rule_title": "AWS Console Login by New User"
},
"risk": {
"risk_object": "user",
"risk_object_type": [
"user"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "5 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventName=ConsoleLogin | rename userIdentity.arn as user |stats earliest(_time) as earliest latest(_time) as latest by user | inputlookup append=t previously_seen_users_console_logins.csv | stats min(earliest) as earliest max(latest) as latest by user | eval userStatus=if(earliest >= relative_time(now(), \"-70m@m\"), \"First Time Logging into AWS Console\",\"Previously Seen User\") | convert ctime(earliest) ctime(latest) | where userStatus =\"First Time Logging into AWS Console\" ",
"suppress": {
"suppress_fields": "user",
"suppress_period": "86400s"
}
}
}
},
"eli5": "In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console and group the events using ARN value. Using the <code>previously_seen_users_console_logins.csv</code> lookup file created using the support search, we compare the ARN to all the previously seen users logging into the AWS console. The <code>eval</code> and <code>if</code> functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour.",
"entities": [
"user"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the \"Previously seen users in CloudTrail\" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run \"Update previously seen users in CloudTrail\" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines.",
"id": "ada0f478-84a8-4641-a3f3-d82362dffd75",
"investigations": [
{
"id": "bc91a8cd-35e7-4bb2-6140-e756cc46fd71",
"name": "AWS Investigate User Activities By ARN",
"type": "splunk"
}
],
"known_false_positives": "When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
"risk": {
"risk_object": "user",
"risk_object_type": [
"user"
],
"risk_score": 30
},
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
"schedule": {
"cron_schedule": "5 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "sourcetype=aws:cloudtrail eventName=ConsoleLogin | rename userIdentity.arn as user |stats earliest(_time) as earliest latest(_time) as latest by user | inputlookup append=t previously_seen_users_console_logins.csv | stats min(earliest) as earliest max(latest) as latest by user | eval userStatus=if(earliest >= relative_time(now(), \"-70m@m\"), \"First Time Logging into AWS Console\",\"Previously Seen User\") | convert ctime(earliest) ctime(latest) | where userStatus =\"First Time Logging into AWS Console\" ",
"suppress": {
"suppress_fields": "user",
"suppress_period": "86400s"
}
],
"mappings": {
"cis20": [
"CIS 16"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Credential Access"
],
"nist": [
"DE.DP",
"DE.AE"
]
}
}
},
"eli5": "In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen users logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour.",
"entities": [
"user"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the \"Previously seen users in CloudTrail\" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run \"Update previously seen users in CloudTrail\" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines.",
"id": "ada0f478-84a8-4641-a3f3-d82362dffd75",
"investigations": [
{
"id": "bc91a8cd-35e7-4bb2-6140-e756cc46fd71",
"name": "AWS Investigate User Activities By ARN",
"type": "splunk"
}
],
"known_false_positives": "When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
},
"modification_date": "2018-04-30",
"name": "Detect new user AWS Console Login",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
}
],
"mappings": {
"cis20": [
"CIS 16"
],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Credential Access"
],
"nist": [
"DE.DP",
"DE.AE"
]
},
"modification_date": "2018-04-30",
"name": "Detect new user AWS Console Login",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/new_aws_console_login_city_by_user.json
+109 -109
View File
@@ -1,117 +1,117 @@
{
"asset_type": "AWS Instance",
"baselines": [
{
"id": "fc0edc95-ff2b-48b0-9f6f-63da3789fd03",
"name": "Previously seen users in CloudTrail",
"type": "splunk"
},
{
"id": "06c036e6-d6d7-4daa-bd76-411c3d356031",
"name": "Update previously seen users in CloudTrail",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2018-04-24",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
"asset_type": "AWS Instance",
"baselines": [
{
"id": "fc0edc95-ff2b-48b0-9f6f-63da3789fd03",
"name": "Previously seen users in CloudTrail",
"type": "splunk"
},
"description": "This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "user",
"rule_description": "A user has logged into the AWS console from a new city.",
"rule_title": "AWS Console Login by User from New City"
},
"risk": {
"risk_object": "user",
"risk_object_type": [
"user"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "5 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| inputlookup previously_seen_users_console_logins.csv | stats min(earliest) as earliest max(latest) as latest by user City | join user type=outer [| inputlookup previously_seen_users_console_logins.csv | stats min(earliest) AS earliestseen by user | fields earliestseen user] | eval userStatus=if(earliest >= relative_time(now(), \"@d\"), \"New City\",\"Previously Seen City\") | eval UserData=if(earliestseen >= relative_time(now(), \"@d\") OR isnull(earliestseen), \"New User\",\"Old User\") | where userStatus=\"New City\" AND UserData=\"Old User\" | convert ctime(earliest) ctime(latest) ctime(earliestseen) | table user City userStatus earliest latest earliestseen",
"suppress": {
"suppress_fields": "user",
"suppress_period": "86400s"
}
}
}
},
"eli5": "In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console from a new city and group the events using ARN value. Using the <code>previously_seen_users_console_logins.csv</code> 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 <code>eval</code> and <code>if</code> functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour.",
"entities": [
"user"
{
"id": "06c036e6-d6d7-4daa-bd76-411c3d356031",
"name": "Update previously seen users in CloudTrail",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2018-04-24",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the \"Previously seen users in CloudTrail\" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run \"Update previously seen users in CloudTrail\" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines.",
"id": "121b0b11-f8ac-4ed6-a132-3800ca4fc07a",
"investigations": [
{
"id": "bc91a8cd-35e7-4bb2-6140-e756cc46fd71",
"name": "AWS Investigate User Activities By ARN",
"type": "splunk"
}
"data_sourcetypes": [
"aws:cloudtrail"
],
"known_false_positives": "When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "user",
"rule_description": "A user has logged into the AWS console from a new city.",
"rule_title": "AWS Console Login by User from New City"
},
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
}
],
"mappings": {
"cis20": [
"CIS 16"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Credential Access"
],
"nist": [
"DE.DP",
"DE.AE"
]
},
"modification_date": "2018-04-30",
"name": "Detect AWS Console Login by User from New City",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
"risk": {
"risk_object": "user",
"risk_object_type": [
"user"
],
"risk_score": 30
},
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
"schedule": {
"cron_schedule": "5 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| inputlookup previously_seen_users_console_logins.csv | stats min(earliest) as earliest max(latest) as latest by user City | join user type=outer [| inputlookup previously_seen_users_console_logins.csv | stats min(earliest) AS earliestseen by user | fields earliestseen user] | eval userStatus=if(earliest >= relative_time(now(), \"@d\"), \"New City\",\"Previously Seen City\") | eval UserData=if(earliestseen >= relative_time(now(), \"@d\") OR isnull(earliestseen), \"New User\",\"Old User\") | where userStatus=\"New City\" AND UserData=\"Old User\" | convert ctime(earliest) ctime(latest) ctime(earliestseen) | table user City userStatus earliest latest earliestseen",
"suppress": {
"suppress_fields": "user",
"suppress_period": "86400s"
}
}
}
},
"eli5": "In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console from a new city and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen ARN and city combinations logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour.",
"entities": [
"user"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the \"Previously seen users in CloudTrail\" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run \"Update previously seen users in CloudTrail\" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines.",
"id": "121b0b11-f8ac-4ed6-a132-3800ca4fc07a",
"investigations": [
{
"id": "bc91a8cd-35e7-4bb2-6140-e756cc46fd71",
"name": "AWS Investigate User Activities By ARN",
"type": "splunk"
}
],
"known_false_positives": "When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
},
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
}
],
"mappings": {
"cis20": [
"CIS 16"
],
"responses": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Credential Access"
],
"nist": [
"DE.DP",
"DE.AE"
]
},
"modification_date": "2018-04-30",
"name": "Detect AWS Console Login by User from New City",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
},
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
}
],
"responses": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/new_aws_console_login_country_by_user.json
+108 -108
View File
@@ -1,116 +1,116 @@
{
"asset_type": "AWS Instance",
"baselines": [
{
"id": "fc0edc95-ff2b-48b0-9f6f-63da3789fd03",
"name": "Previously seen users in CloudTrail",
"type": "splunk"
},
{
"id": "06c036e6-d6d7-4daa-bd76-411c3d356031",
"name": "Update previously seen users in CloudTrail",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2018-04-24",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
"asset_type": "AWS Instance",
"baselines": [
{
"id": "fc0edc95-ff2b-48b0-9f6f-63da3789fd03",
"name": "Previously seen users in CloudTrail",
"type": "splunk"
},
"description": "This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "user",
"rule_description": "A user has logged into the AWS console from a new country.",
"rule_title": "AWS Console Login by User from New Country"
},
"risk": {
"risk_object": "user",
"risk_object_type": [
"user"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "5 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| inputlookup previously_seen_users_console_logins.csv | stats min(earliest) as earliest max(latest) as latest by user Country | join user type=outer [| inputlookup previously_seen_users_console_logins.csv | stats min(earliest) AS earliestseen by user | fields earliestseen user] | eval userStatus=if(earliest >= relative_time(now(), \"@d\"), \"New Country\",\"Previously Seen Country\") | eval UserData=if(earliestseen >= relative_time(now(), \"@d\") OR isnull(earliestseen), \"New User\",\"Old User\") | where userStatus=\"New Country\" AND UserData=\"Old User\" | convert ctime(earliest) ctime(latest) ctime(earliestseen) | table user Country userStatus earliest latest earliestseen",
"suppress": {
"suppress_fields": "user",
"suppress_period": "86400s"
}
}
}
},
"eli5": "In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console from a new country and group the events using ARN value. Using the <code>previously_seen_users_console_logins.csv</code> 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 <code>eval</code> and <code>if</code> functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour.",
"entities": [
"user"
{
"id": "06c036e6-d6d7-4daa-bd76-411c3d356031",
"name": "Update previously seen users in CloudTrail",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2018-04-24",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the \"Previously seen users in CloudTrail\" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run \"Update previously seen users in CloudTrail\" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines.",
"id": "67bd3def-c41c-4bf6-837b-ae196b4257c6",
"investigations": [
{
"id": "bc91a8cd-35e7-4bb2-6140-e756cc46fd71",
"name": "AWS Investigate User Activities By ARN",
"type": "splunk"
}
"data_sourcetypes": [
"aws:cloudtrail"
],
"known_false_positives": "When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "user",
"rule_description": "A user has logged into the AWS console from a new country.",
"rule_title": "AWS Console Login by User from New Country"
},
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
}
],
"mappings": {
"cis20": [
"CIS 16"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Credential Access"
],
"nist": [
"DE.DP",
"DE.AE"
]
},
"modification_date": "2018-04-30",
"name": "Detect AWS Console Login by User from New Country",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
"risk": {
"risk_object": "user",
"risk_object_type": [
"user"
],
"risk_score": 30
},
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
"schedule": {
"cron_schedule": "5 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| inputlookup previously_seen_users_console_logins.csv | stats min(earliest) as earliest max(latest) as latest by user Country | join user type=outer [| inputlookup previously_seen_users_console_logins.csv | stats min(earliest) AS earliestseen by user | fields earliestseen user] | eval userStatus=if(earliest >= relative_time(now(), \"@d\"), \"New Country\",\"Previously Seen Country\") | eval UserData=if(earliestseen >= relative_time(now(), \"@d\") OR isnull(earliestseen), \"New User\",\"Old User\") | where userStatus=\"New Country\" AND UserData=\"Old User\" | convert ctime(earliest) ctime(latest) ctime(earliestseen) | table user Country userStatus earliest latest earliestseen",
"suppress": {
"suppress_fields": "user",
"suppress_period": "86400s"
}
}
}
},
"eli5": "In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console from a new country and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen ARN and country combinations logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour.",
"entities": [
"user"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the \"Previously seen users in CloudTrail\" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run \"Update previously seen users in CloudTrail\" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines.",
"id": "67bd3def-c41c-4bf6-837b-ae196b4257c6",
"investigations": [
{
"id": "bc91a8cd-35e7-4bb2-6140-e756cc46fd71",
"name": "AWS Investigate User Activities By ARN",
"type": "splunk"
}
],
"known_false_positives": "When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
},
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
}
],
"mappings": {
"cis20": [
"CIS 16"
],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Credential Access"
],
"nist": [
"DE.DP",
"DE.AE"
]
},
"modification_date": "2018-04-30",
"name": "Detect AWS Console Login by User from New Country",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
},
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
}
],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/new_aws_console_login_region_by_user.json
+109 -109
View File
@@ -1,117 +1,117 @@
{
"asset_type": "AWS Instance",
"baselines": [
{
"id": "fc0edc95-ff2b-48b0-9f6f-63da3789fd03",
"name": "Previously seen users in CloudTrail",
"type": "splunk"
},
{
"id": "06c036e6-d6d7-4daa-bd76-411c3d356031",
"name": "Update previously seen users in CloudTrail",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2018-04-24",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"data_sourcetypes": [
"aws:cloudtrail"
],
"providing_technologies": [
"AWS"
]
"asset_type": "AWS Instance",
"baselines": [
{
"id": "fc0edc95-ff2b-48b0-9f6f-63da3789fd03",
"name": "Previously seen users in CloudTrail",
"type": "splunk"
},
"description": "This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "user",
"rule_description": "A user has logged into the AWS console from a new region.",
"rule_title": "AWS Console Login by User from New Region"
},
"risk": {
"risk_object": "user",
"risk_object_type": [
"user"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "5 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| inputlookup previously_seen_users_console_logins.csv | stats min(earliest) as earliest max(latest) as latest by user Region | join user type=outer [| inputlookup previously_seen_users_console_logins.csv | stats min(earliest) AS earliestseen by user | fields earliestseen user] | eval userStatus=if(earliest >= relative_time(now(), \"@d\"), \"New Region\",\"Previously Seen Region\") | eval UserData=if(earliestseen >= relative_time(now(), \"@d\") OR isnull(earliestseen), \"New User\",\"Old User\") | where userStatus=\"New Region\" AND UserData=\"Old User\" | convert ctime(earliest) ctime(latest) ctime(earliestseen) | table user Region userStatus earliest latest earliestseen",
"suppress": {
"suppress_fields": "user",
"suppress_period": "86400s"
}
}
}
},
"eli5": "In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console from a new region and group the events using ARN value. Using the <code>previously_seen_users_console_logins.csv</code> 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 <code>eval</code> and <code>if</code> functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour.",
"entities": [
"user"
{
"id": "06c036e6-d6d7-4daa-bd76-411c3d356031",
"name": "Update previously seen users in CloudTrail",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2018-04-24",
"data_metadata": {
"data_source": [
"AWS CloudTrail logs"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the \"Previously seen users in CloudTrail\" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run \"Update previously seen users in CloudTrail\" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines.",
"id": "9f31aa8e-e37c-46bc-bce1-8b3be646d026",
"investigations": [
{
"id": "bc91a8cd-35e7-4bb2-6140-e756cc46fd71",
"name": "AWS Investigate User Activities By ARN",
"type": "splunk"
}
"data_sourcetypes": [
"aws:cloudtrail"
],
"known_false_positives": "When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
"providing_technologies": [
"AWS"
]
},
"description": "This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "user",
"rule_description": "A user has logged into the AWS console from a new region.",
"rule_title": "AWS Console Login by User from New Region"
},
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
}
],
"mappings": {
"cis20": [
"CIS 16"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Credential Access"
],
"nist": [
"DE.DP",
"DE.AE"
]
},
"modification_date": "2018-04-30",
"name": "Detect AWS Console Login by User from New Region",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
"risk": {
"risk_object": "user",
"risk_object_type": [
"user"
],
"risk_score": 30
},
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
"schedule": {
"cron_schedule": "5 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| inputlookup previously_seen_users_console_logins.csv | stats min(earliest) as earliest max(latest) as latest by user Region | join user type=outer [| inputlookup previously_seen_users_console_logins.csv | stats min(earliest) AS earliestseen by user | fields earliestseen user] | eval userStatus=if(earliest >= relative_time(now(), \"@d\"), \"New Region\",\"Previously Seen Region\") | eval UserData=if(earliestseen >= relative_time(now(), \"@d\") OR isnull(earliestseen), \"New User\",\"Old User\") | where userStatus=\"New Region\" AND UserData=\"Old User\" | convert ctime(earliest) ctime(latest) ctime(earliestseen) | table user Region userStatus earliest latest earliestseen",
"suppress": {
"suppress_fields": "user",
"suppress_period": "86400s"
}
}
}
},
"eli5": "In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console from a new region and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen ARN and region combinations logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour.",
"entities": [
"user"
],
"how_to_implement": "You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the \"Previously seen users in CloudTrail\" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run \"Update previously seen users in CloudTrail\" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines.",
"id": "9f31aa8e-e37c-46bc-bce1-8b3be646d026",
"investigations": [
{
"id": "bc91a8cd-35e7-4bb2-6140-e756cc46fd71",
"name": "AWS Investigate User Activities By ARN",
"type": "splunk"
}
],
"known_false_positives": "When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
},
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
}
],
"mappings": {
"cis20": [
"CIS 16"
],
"responses": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Credential Access"
],
"nist": [
"DE.DP",
"DE.AE"
]
},
"modification_date": "2018-04-30",
"name": "Detect AWS Console Login by User from New Region",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
},
{
"company": "Splunk",
"email": "jbrewer@splunk.com",
"name": "Jason Brewer"
}
],
"responses": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/new_connections_to_routers.json
+114 -114
View File
@@ -1,119 +1,119 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2017-07-18",
"data_metadata": {
"data_models": [
"Authentication"
],
"data_source": [
"Network Equipment"
],
"providing_technologies": [
"Active Directory",
"Palo Alto Firewall"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2017-07-18",
"data_metadata": {
"data_models": [
"Authentication"
],
"data_source": [
"Network Equipment"
],
"providing_technologies": [
"Active Directory",
"Palo Alto Firewall"
]
},
"description": "The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user",
"rule_description": "This search detects new connections made to the router devices at $dest$",
"rule_title": "Detected a New Router Login"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 20
},
"schedule": {
"cron_schedule": "0 0 * * *",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count earliest(_time) as earliest latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router by Authentication.dest Authentication.user| eval isOutlier=if(earliest >= relative_time(now(), \"-30d@d\"), 1, 0) | where isOutlier=1| `ctime(earliest)`| `ctime(latest)` | `drop_dm_object_name(\"Authentication\")`",
"suppress": {
"suppress_fields": "dest,user",
"suppress_period": "86400s"
}
}
}
},
"eli5": "Attackers will often attempt to compromise network devices such as routers for a variety of nefarious purposes, including modifying VPN settings or re-routing network traffic. Typically, only a relatively small number of user accounts log into these devices on a regular basis. This search identifies 'new' connections to your routers by checking to see if a similar login was made in the last 30 days. Routers are identified by checking the IP address against those categorized as a \"router\" in the ES assets and identity framework.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must ensure the network router devices are categorized as \"router\" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure.",
"id": "104658f4-afdc-499e-9719-17243rr826f1",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user",
"rule_description": "This search detects new connections made to the router devices at $dest$",
"rule_title": "Detected a New Router Login"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 20
},
"schedule": {
"cron_schedule": "0 0 * * *",
"earliest_time": "-30d@d",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count earliest(_time) as earliest latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router by Authentication.dest Authentication.user| eval isOutlier=if(earliest >= relative_time(now(), \"-30d@d\"), 1, 0) | where isOutlier=1| `ctime(earliest)`| `ctime(latest)` | `drop_dm_object_name(\"Authentication\")`",
"suppress": {
"suppress_fields": "dest,user",
"suppress_period": "86400s"
}
}
}
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"eli5": "Attackers will often attempt to compromise network devices such as routers for a variety of nefarious purposes, including modifying VPN settings or re-routing network traffic. Typically, only a relatively small number of user accounts log into these devices on a regular basis. This search identifies 'new' connections to your routers by checking to see if a similar login was made in the last 30 days. Routers are identified by checking the IP address against those categorized as a \"router\" in the ES assets and identity framework.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must ensure the network router devices are categorized as \"router\" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure.",
"id": "104658f4-afdc-499e-9719-17243rr826f1",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Legitimate router connections may appear as new connections",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 11"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"nist": [
"PR.PT",
"PR.AC",
"PR.IP"
]
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
"modification_date": "2017-09-12",
"name": "Detect New Login Attempts to Routers",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Legitimate router connections may appear as new connections",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 11"
],
"references": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"kill_chain_phases": [
"Actions on Objectives"
],
"nist": [
"PR.PT",
"PR.AC",
"PR.IP"
]
},
"modification_date": "2017-09-12",
"name": "Detect New Login Attempts to Routers",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"references": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/outbound_smb_connections.json
+127 -127
View File
@@ -1,133 +1,133 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2018-03-20",
"data_metadata": {
"data_models": [
"Network_Traffic"
],
"data_source": [
"Network Communications"
],
"providing_technologies": [
"Bro",
"Splunk Stream"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2018-03-20",
"data_metadata": {
"data_models": [
"Network_Traffic"
],
"data_source": [
"Network Communications"
],
"providing_technologies": [
"Bro",
"Splunk Stream"
]
},
"description": "This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src_ip, dest_ip",
"rule_description": "Outbound SMB network traffic detected.",
"rule_title": "Outbound SMB traffic from $src_ip$ to $dest_ip$ detected"
},
"risk": {
"risk_object": "src_ip",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count earliest(_time) as earliest latest(_time) as latest values(All_Traffic.action) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb) by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name(\"All_Traffic\")` | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | convert ctime(earliest) ctime(latest)",
"suppress": {
"suppress_fields": "src_ip",
"suppress_period": "28800s"
}
}
}
},
"eli5": "In this search, we are looking for the network connections that were not blocked by the firewall and that are destined for destination port 139 or 445. We then filter out events that have Classless Inter-Domain Routing (CIDR) blocks categorized as internal in the `assets_by_cidr.csv` lookup file which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. Since we are only looking for outbound traffic from the hosts made to the Internet, we filter out traffic whose destination IP address is private.",
"entities": [
"src_ip"
],
"how_to_implement": "In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have good understanding of how your network segments are designed, and be able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model",
"id": "7f5fb3e1-4209-414-90db-0ec21b936378",
"investigations": [
{
"id": "9925d08f-561e-4faa-8912-e3888a842341",
"name": "Get Process Information For Port Activity",
"type": "splunk"
},
"description": "This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src_ip, dest_ip",
"rule_description": "Outbound SMB network traffic detected.",
"rule_title": "Outbound SMB traffic from $src_ip$ to $dest_ip$ detected"
},
"risk": {
"risk_object": "src_ip",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count earliest(_time) as earliest latest(_time) as latest values(All_Traffic.action) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb) by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name(\"All_Traffic\")` | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | convert ctime(earliest) ctime(latest)",
"suppress": {
"suppress_fields": "src_ip",
"suppress_period": "28800s"
}
}
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"eli5": "In this search, we are looking for the network connections that were not blocked by the firewall and that are destined for destination port 139 or 445. We then filter out events that have Classless Inter-Domain Routing (CIDR) blocks categorized as internal in the <code>assets_by_cidr.csv</code> lookup file which is located in <code>$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/</code>. Since we are only looking for outbound traffic from the hosts made to the Internet, we filter out traffic whose destination IP address is private.",
"entities": [
"src_ip"
],
"how_to_implement": "In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have good understanding of how your network segments are designed, and be able to distinguish internal from external address space. Add a category named <code>internal</code> to the CIDRs that host the company's assets in <code>assets_by_cidr.csv</code> lookup file, which is located in <code>$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/</code>. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model",
"id": "7f5fb3e1-4209-414-90db-0ec21b936378",
"investigations": [
{
"id": "9925d08f-561e-4faa-8912-e3888a842341",
"name": "Get Process Information For Port Activity",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as <code>internal</code> in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 12"
],
"kill_chain_phases": [
"Actions on Objectives",
"Command and Control"
],
"mitre_attack": [
"Commonly Used Port",
"Credential Access",
"Lateral Movement"
],
"nist": [
"DE.CM"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"modification_date": "2018-03-20",
"name": "Detect Outbound SMB Traffic",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 12"
],
"references": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
"kill_chain_phases": [
"Actions on Objectives",
"Command and Control"
],
"mitre_attack": [
"Commonly Used Port",
"Credential Access",
"Lateral Movement"
],
"nist": [
"DE.CM"
]
},
"modification_date": "2018-03-20",
"name": "Detect Outbound SMB Traffic",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"references": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/overwrite_accessibility_binaries.json
+128 -128
View File
@@ -1,133 +1,133 @@
{
"asset_type": "Endpoint",
"confidence": "high",
"creation_date": "2017-12-07",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon"
]
"asset_type": "Endpoint",
"confidence": "high",
"creation_date": "2017-12-07",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon"
]
},
"description": "Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, file_name",
"rule_description": "A file, $file_name$, was created in the default shim database directory on $dest.",
"rule_title": "Modification to accessibility binary, $file_path$, was detected on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 40
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) by Filesystem.file_name Filesystem.dest | `drop_dm_object_name(Filesystem)` | `ctime(lastTime)` | `ctime(firstTime)`",
"suppress": {
"suppress_fields": "dest",
"suppress_period": "14400s"
}
}
}
},
"eli5": "This search returns all the different accessibility binaries that have been modified for each Windows host.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.",
"id": "13c2f6c3-10c5-4deb-9ba1-7c4460ebe4ae",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, file_name",
"rule_description": "A file, $file_name$, was created in the default shim database directory on $dest.",
"rule_title": "Modification to accessibility binary, $file_path$, was detected on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 40
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) by Filesystem.file_name Filesystem.dest | `drop_dm_object_name(Filesystem)` | `ctime(lastTime)` | `ctime(firstTime)`",
"suppress": {
"suppress_fields": "dest",
"suppress_period": "14400s"
}
}
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"eli5": "This search returns all the different accessibility binaries that have been modified for each Windows host.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.",
"id": "13c2f6c3-10c5-4deb-9ba1-7c4460ebe4ae",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Microsoft may provide updates to these binaries. Verify that these changes do not correspond with your normal software update cycle.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Persistence",
"Accessibility Features"
],
"nist": [
"PR.PT",
"DE.CM"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"modification_date": "2018-11-15",
"name": "Overwriting Accessibility Binaries",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Microsoft may provide updates to these binaries. Verify that these changes do not correspond with your normal software update cycle.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Persistence",
"Accessibility Features"
],
"nist": [
"PR.PT",
"DE.CM"
]
},
"modification_date": "2018-11-15",
"name": "Overwriting Accessibility Binaries",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
detections/password_sharing.json
+102 -102
View File
@@ -1,110 +1,110 @@
{
"asset_type": "account",
"confidence": "medium",
"creation_date": "2018-07-12",
"data_metadata": {
"data_source": [
"Network Communications"
],
"data_sourcetypes": [
"stream:http"
],
"providing_technologies": [
"Splunk Stream",
"Palo Alto Firewall",
"Bro"
]
},
"description": "This search is used to identify user accounts that share a common password.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "user",
"rule_description": "This search is used to identify user accounts, $user$, that share common passwords",
"rule_title": "Web Fraud Detection: Password Sharing Across Accounts"
},
"risk": {
"risk_object": "user",
"risk_object_type": [
"other"
],
"risk_score": 10
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "sourcetype=stream:http http_content_type=text* uri=/magento2/customer/account/loginPost* | rex field=form_data \"login\\[username\\]=(?<Username>[^&|^$]+)\" | rex field=form_data \"login\\[password\\]=(?<Password>[^&|^$]+)\" | stats dc(Username) as UniqueUsernames values(Username) as user list(src_ip) as src_ip by Password|where UniqueUsernames>5",
"suppress": {
"suppress_fields": "user",
"suppress_period": "3600s"
}
}
}
},
"eli5": "A common password across user accounts generally indicates that the users are choosing poor passwords or that a fraudster has a common password across multiple accounts embedded within a script. The search will extract the username and password information from the form_data field, then calculate the number and values for usernames that have the same passwords. Finally, it outputs the values where the unique usernames sharing passwords are greater than 5",
"entities": [
"user"
"asset_type": "account",
"confidence": "medium",
"creation_date": "2018-07-12",
"data_metadata": {
"data_source": [
"Network Communications"
],
"how_to_implement": "We need to start with a dataset that allows us to see the values of usernames and passwords that users are submitting to the website hosting the Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized or hashed value of a password is acceptable and certainly preferable to a clear-text password. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream.",
"id": "31337a1a-53b9-4e05-96e9-55c934cb71d3",
"investigations": [
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
"data_sourcetypes": [
"stream:http"
],
"providing_technologies": [
"Splunk Stream",
"Palo Alto Firewall",
"Bro"
]
},
"description": "This search is used to identify user accounts that share a common password.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "user",
"rule_description": "This search is used to identify user accounts, $user$, that share common passwords",
"rule_title": "Web Fraud Detection: Password Sharing Across Accounts"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
"risk": {
"risk_object": "user",
"risk_object_type": [
"other"
],
"risk_score": 10
},
{
"id": "bc91a8cf-35e7-4bb2-1120-e756cc06fd89",
"name": "Get Web Session Information via session_id",
"type": "splunk"
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
{
"id": "5df39b3f-447d-4869-b673-8f45ad4616fe",
"name": "Get Emails From Specific Sender",
"type": "splunk"
"search": "sourcetype=stream:http http_content_type=text* uri=/magento2/customer/account/loginPost* | rex field=form_data \"login\\[username\\]=(?<Username>[^&|^$]+)\" | rex field=form_data \"login\\[password\\]=(?<Password>[^&|^$]+)\" | stats dc(Username) as UniqueUsernames values(Username) as user list(src_ip) as src_ip by Password|where UniqueUsernames>5",
"suppress": {
"suppress_fields": "user",
"suppress_period": "3600s"
}
],
"known_false_positives": "As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamoluous behavior.",
"maintainers": [
{
"company": "Splunk",
"email": "Mayhem@splunk.com",
"name": "Jim Apger"
}
],
"mappings": {
"cis20": [
"CIS 16"
],
"nist": [
"DE.DP"
]
}
}
},
"eli5": "A common password across user accounts generally indicates that the users are choosing poor passwords or that a fraudster has a common password across multiple accounts embedded within a script. The search will extract the username and password information from the form_data field, then calculate the number and values for usernames that have the same passwords. Finally, it outputs the values where the unique usernames sharing passwords are greater than 5",
"entities": [
"user"
],
"how_to_implement": "We need to start with a dataset that allows us to see the values of usernames and passwords that users are submitting to the website hosting the Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized or hashed value of a password is acceptable and certainly preferable to a clear-text password. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream.",
"id": "31337a1a-53b9-4e05-96e9-55c934cb71d3",
"investigations": [
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
"modification_date": "2018-10-08",
"name": "Web Fraud - Password Sharing Across Accounts",
"original_authors": [
{
"company": "Splunk",
"email": "Mayhem@splunk.com",
"name": "Jim Apger"
}
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-1120-e756cc06fd89",
"name": "Get Web Session Information via session_id",
"type": "splunk"
},
{
"id": "5df39b3f-447d-4869-b673-8f45ad4616fe",
"name": "Get Emails From Specific Sender",
"type": "splunk"
}
],
"known_false_positives": "As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamoluous behavior.",
"maintainers": [
{
"company": "Splunk",
"email": "Mayhem@splunk.com",
"name": "Jim Apger"
}
],
"mappings": {
"cis20": [
"CIS 16"
],
"references": [
"https://en.wikipedia.org/wiki/Session_ID",
"https://en.wikipedia.org/wiki/Session_(computer_science)",
"https://en.wikipedia.org/wiki/HTTP_cookie",
"https://splunkbase.splunk.com/app/1809/"
],
"security_domain": "threat",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"nist": [
"DE.DP"
]
},
"modification_date": "2018-10-08",
"name": "Web Fraud - Password Sharing Across Accounts",
"original_authors": [
{
"company": "Splunk",
"email": "Mayhem@splunk.com",
"name": "Jim Apger"
}
],
"references": [
"https://en.wikipedia.org/wiki/Session_ID",
"https://en.wikipedia.org/wiki/Session_(computer_science)",
"https://en.wikipedia.org/wiki/HTTP_cookie",
"https://splunkbase.splunk.com/app/1809/"
],
"security_domain": "threat",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/path_interception_program.exe.json
+129 -129
View File
@@ -1,134 +1,134 @@
{
"asset_type": "",
"confidence": "medium",
"creation_date": "2017-11-16",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Tanium",
"Ziften"
]
"asset_type": "",
"confidence": "medium",
"creation_date": "2017-11-16",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Tanium",
"Ziften"
]
},
"description": "The search is looking for the creation of program.exe in the C: drive. The creation of this file in that location may be driven by a motive to perform path interception.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, file_path, file_name",
"rule_description": "A potentially malicious file program.exe was detected on the C: drive. The creation of this file is often associated with a motive to perform a path interception attack. ",
"rule_title": "Path Interception attempt discovered $dest$ via creation of program.exe"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "30 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `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)` | `ctime(lastTime)` | `ctime(firstTime)`",
"suppress": {
"suppress_fields": "dest, file_path, file_name",
"suppress_period": "86400s"
}
}
}
},
"eli5": "This search queries the Endpoint file-system data model node to list out all the values of destination machines, as well as the values of file hashes and file paths that have the file \"program.exe\" in the C: drive. Path interception occurs when an executable is placed in a specific path so that it is executed by an application instead of by the intended target. In this case, applications vulnerable to path interception (because of unquoted service paths with spaces in Windows registry) allow attackers to execute maliciously crafted program.exes.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes.",
"id": "c77162d3-f93c-45cc-80c8-22f6v5264g9f",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "The search is looking for the creation of program.exe in the C: drive. The creation of this file in that location may be driven by a motive to perform path interception.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, file_path, file_name",
"rule_description": "A potentially malicious file program.exe was detected on the C: drive. The creation of this file is often associated with a motive to perform a path interception attack. ",
"rule_title": "Path Interception attempt discovered $dest$ via creation of program.exe"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "30 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `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)` | `ctime(lastTime)` | `ctime(firstTime)`",
"suppress": {
"suppress_fields": "dest, file_path, file_name",
"suppress_period": "86400s"
}
}
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"eli5": "This search queries the Endpoint file-system data model node to list out all the values of destination machines, as well as the values of file hashes and file paths that have the file \"program.exe\" in the C: drive. Path interception occurs when an executable is placed in a specific path so that it is executed by an application instead of by the intended target. In this case, applications vulnerable to path interception (because of unquoted service paths with spaces in Windows registry) allow attackers to execute maliciously crafted program.exes.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes.",
"id": "c77162d3-f93c-45cc-80c8-22f6v5264g9f",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "It is unlikely that a normal user may create and place this file in the C: drive. Confirm with the user.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Privilege Escalation",
"Persistence"
],
"nist": [
"PR.PT",
"DE.CM"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"modification_date": "2018-11-15",
"name": "Detect Path Interception By Creation Of program.exe",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "It is unlikely that a normal user may create and place this file in the C: drive. Confirm with the user.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Privilege Escalation",
"Persistence"
],
"nist": [
"PR.PT",
"DE.CM"
]
},
"modification_date": "2018-11-15",
"name": "Detect Path Interception By Creation Of program.exe",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
detections/port_monitor_via_registry.json
+136 -136
View File
@@ -1,141 +1,141 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2017-12-01",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2017-12-01",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon"
]
},
"description": "This search looks for registry activity associated with modifications to the registry key `HKLM\\SYSTEM\\CurrentControlSet\\Control\\Print\\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user, registry_path",
"rule_description": "A registry key associated with adding print monitors can potentially be misused by giving it a path of a malicious .dll in the registry.",
"rule_title": "Registry Key changes for Print Monitors detected on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND Registry.registry_path=\"*CurrentControlSet\\\\Control\\\\Print\\\\Monitors*\" by Registry.dest, Registry.registry_key_name Registry.status Registry.user Registry.registry_path Registry.action | `drop_dm_object_name(Registry)`",
"suppress": {
"suppress_fields": "dest, registry_path",
"suppress_period": "86400s"
}
}
}
},
"eli5": "In this search, we look for modifications to registry keys used for adding print-monitor entries on Microsoft platforms via the `registry_path` field in the endpoint data model. It then provides the destination, command used to initiate the change, the user who conducted this activity, the resource affected (registry_key_name), and the entire path of the registry.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications.",
"id": "f5f6af30-7ba7-4295-bfe9-07de87c01bbc",
"investigations": [
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41xf9",
"name": "Get Registry Activities",
"type": "splunk"
},
"description": "This search looks for registry activity associated with modifications to the registry key <code>HKLM\\SYSTEM\\CurrentControlSet\\Control\\Print\\Monitors</code>. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user, registry_path",
"rule_description": "A registry key associated with adding print monitors can potentially be misused by giving it a path of a malicious .dll in the registry.",
"rule_title": "Registry Key changes for Print Monitors detected on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND Registry.registry_path=\"*CurrentControlSet\\\\Control\\\\Print\\\\Monitors*\" by Registry.dest, Registry.registry_key_name Registry.status Registry.user Registry.registry_path Registry.action | `drop_dm_object_name(Registry)`",
"suppress": {
"suppress_fields": "dest, registry_path",
"suppress_period": "86400s"
}
}
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"eli5": "In this search, we look for modifications to registry keys used for adding print-monitor entries on Microsoft platforms via the <code>registry_path</code> field in the endpoint data model. It then provides the destination, command used to initiate the change, the user who conducted this activity, the resource affected (registry_key_name), and the entire path of the registry.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications.",
"id": "f5f6af30-7ba7-4295-bfe9-07de87c01bbc",
"investigations": [
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41xf9",
"name": "Get Registry Activities",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "You will encounter noise from legitimate print-monitor registry entries.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8",
"CIS 5"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Persistence",
"Privilege Escalation",
"Local Port Monitor"
],
"nist": [
"PR.PT",
"DE.CM",
"PR.AC"
]
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"modification_date": "2018-11-02",
"name": "Monitor Registry Keys for Print Monitors",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "You will encounter noise from legitimate print-monitor registry entries.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8",
"CIS 5"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Persistence",
"Privilege Escalation",
"Local Port Monitor"
],
"nist": [
"PR.PT",
"DE.CM",
"PR.AC"
]
},
"modification_date": "2018-11-02",
"name": "Monitor Registry Keys for Print Monitors",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/process_with_double_extension.json
+133 -133
View File
@@ -1,138 +1,138 @@
{
"asset_type": "Endpoint",
"confidence": "high",
"creation_date": "2018-01-26",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
"asset_type": "Endpoint",
"confidence": "high",
"creation_date": "2018-01-26",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the \"real\" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, process",
"rule_description": "The system $dest$ executed a file with a double extension.",
"rule_title": "Process With Multiple Extensions Launched on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 60
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = *.doc.exe OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe by Processes.dest Processes.user Processes.process Processes.parent_process | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name(Processes)`",
"suppress": {
"suppress_fields": "dest, process",
"suppress_period": "28800s"
}
}
}
},
"eli5": "This search uses the \"Application State\" data model to look for process names with specific combinations of double extensions. Relatively straightforward, the search looks for strings in the \"process\" field that match what you're looking for.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node.",
"id": "b06a555e-dce0-417d-a2eb-28a5d8d66ef7",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the \"real\" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, process",
"rule_description": "The system $dest$ executed a file with a double extension.",
"rule_title": "Process With Multiple Extensions Launched on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 60
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = *.doc.exe OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe by Processes.dest Processes.user Processes.process Processes.parent_process | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name(Processes)`",
"suppress": {
"suppress_fields": "dest, process",
"suppress_period": "28800s"
}
}
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"eli5": "This search uses the \"Application State\" data model to look for process names with specific combinations of double extensions. Relatively straightforward, the search looks for strings in the \"process\" field that match what you're looking for.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node.",
"id": "b06a555e-dce0-417d-a2eb-28a5d8d66ef7",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "None identified.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 3",
"CIS 8"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"Persistence",
"Change Default File Association"
],
"nist": [
"DE.CM",
"PR.PT",
"PR.IP"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"modification_date": "2018-11-02",
"name": "Execution of File with Multiple Extensions",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "None identified.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 3",
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"Persistence",
"Change Default File Association"
],
"nist": [
"DE.CM",
"PR.PT",
"PR.IP"
]
},
"modification_date": "2018-11-02",
"name": "Execution of File with Multiple Extensions",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/process_with_spaces_before_extension.json
+133 -133
View File
@@ -1,138 +1,138 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2018-01-26",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2018-01-26",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest",
"rule_description": "The system $dest$ executed a file with spaces before its extension.",
"rule_title": "Process $process$ with spaces before extension Launched on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 60
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Processes.process_path) as process_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = \"* .*\" by Processes.dest Processes.user Processes.process Processes.process_name | `ctime(firstTime)`| `ctime(lastTime)` | `drop_dm_object_name(Processes)`",
"suppress": {
"suppress_fields": "dest,process",
"suppress_period": "28800s"
}
}
}
},
"eli5": "This search uses the endpoint data model to look for process names with at least five spaces between the file name and its extension.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.",
"id": "ab0353e6-a956-420b-b724-a8b4846d5d5a",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest",
"rule_description": "The system $dest$ executed a file with spaces before its extension.",
"rule_title": "Process $process$ with spaces before extension Launched on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 60
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Processes.process_path) as process_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = \"* .*\" by Processes.dest Processes.user Processes.process Processes.process_name | `ctime(firstTime)`| `ctime(lastTime)` | `drop_dm_object_name(Processes)`",
"suppress": {
"suppress_fields": "dest,process",
"suppress_period": "28800s"
}
}
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"eli5": "This search uses the endpoint data model to look for process names with at least five spaces between the file name and its extension.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.",
"id": "ab0353e6-a956-420b-b724-a8b4846d5d5a",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "None identified.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 3",
"CIS 8"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"Persistence",
"Change Default File Association"
],
"nist": [
"DE.CM",
"PR.PT",
"PR.IP"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"modification_date": "2018-01-26",
"name": "Execution of File With Spaces Before Extension",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "None identified.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 3",
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"Persistence",
"Change Default File Association"
],
"nist": [
"DE.CM",
"PR.PT",
"PR.IP"
]
},
"modification_date": "2018-01-26",
"name": "Execution of File With Spaces Before Extension",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/prohibited_apps_spawning_cmdprompt.json
+151 -151
View File
@@ -1,157 +1,157 @@
{
"asset_type": "Endpoint",
"baselines": [
{
"id": "fc0edc95-ff2b-48b0-9f6f-63da3789fd23",
"name": "Previously seen command line arguments",
"type": "splunk"
},
{
"id": "d2a4d85b-fc6a-47a0-82f6-bc1ec2ebc459",
"name": "Baseline of Command Line Length - MLTK",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2017-10-07",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
"asset_type": "Endpoint",
"baselines": [
{
"id": "fc0edc95-ff2b-48b0-9f6f-63da3789fd23",
"name": "Previously seen command line arguments",
"type": "splunk"
},
"description": "This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, process, parent_process",
"rule_description": "A prohibited application from prohibited_apps_launching_cmd.csv was leveraged to launch cmd.exe",
"rule_title": "Prohibited application($parent_process$) used to launch cmd.exe on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 80
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Processes.user) as user values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe by Processes.parent_process_name Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` |search [`prohibited_apps_launching_cmd`]",
"suppress": {
"suppress_fields": "dest, parent_process",
"suppress_period": "86400s"
}
}
{
"id": "d2a4d85b-fc6a-47a0-82f6-bc1ec2ebc459",
"name": "Baseline of Command Line Length - MLTK",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2017-10-07",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, process, parent_process",
"rule_description": "A prohibited application from prohibited_apps_launching_cmd.csv was leveraged to launch cmd.exe",
"rule_title": "Prohibited application($parent_process$) used to launch cmd.exe on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 80
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Processes.user) as user values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe by Processes.parent_process_name Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` |search [`prohibited_apps_launching_cmd`]",
"suppress": {
"suppress_fields": "dest, parent_process",
"suppress_period": "86400s"
}
}
}
},
"eli5": "Obtaining access to the Command-Line Interface (CLI) is typically a primary attacker goal. Once an attacker has obtained the ability to execute code on a target system, they will often further manipulate the system via commands passed to the CLI. It is also unusual for many applications to spawn a command shell during normal operation, while it is often observed if an application has been compromised in some way. As such, it is often beneficial to look for cmd.exe being executed by processes that are often targeted for exploitation, or that would not spawn cmd.exe in any other circumstances. A lookup file is provided to easily modify the processes that are being watched for execution of cmd.exe.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts and populates the Endpoint data model with the resultant dataset. This search includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list of processes that should not be spawning cmd.exe. You can modify this lookup to better suit your environment.",
"id": "dcfd6b40-42f9-469d-a433-2e53f7486664",
"investigations": [
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41xf9",
"name": "Get Registry Activities",
"type": "splunk"
},
"eli5": "Obtaining access to the Command-Line Interface (CLI) is typically a primary attacker goal. Once an attacker has obtained the ability to execute code on a target system, they will often further manipulate the system via commands passed to the CLI. It is also unusual for many applications to spawn a command shell during normal operation, while it is often observed if an application has been compromised in some way. As such, it is often beneficial to look for cmd.exe being executed by processes that are often targeted for exploitation, or that would not spawn cmd.exe in any other circumstances. A lookup file is provided to easily modify the processes that are being watched for execution of cmd.exe.",
"entities": [
"dest"
],
"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, <code>prohibited_apps_launching_cmd.csv</code>, that contains a list of processes that should not be spawning cmd.exe. You can modify this lookup to better suit your environment.",
"id": "dcfd6b40-42f9-469d-a433-2e53f7486664",
"investigations": [
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41xf9",
"name": "Get Registry Activities",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd22",
"name": "Investigate Web Activity From Host",
"type": "splunk"
}
],
"known_false_positives": "There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"kill_chain_phases": [
"Exploitation"
],
"mitre_attack": [
"Execution",
"Command-Line Interface"
],
"nist": [
"PR.PT",
"DE.CM"
]
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"modification_date": "2018-11-15",
"name": "Detect Prohibited Applications Spawning cmd.exe",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd22",
"name": "Investigate Web Activity From Host",
"type": "splunk"
}
],
"known_false_positives": "There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
"kill_chain_phases": [
"Exploitation"
],
"mitre_attack": [
"Execution",
"Command-Line Interface"
],
"nist": [
"PR.PT",
"DE.CM"
]
},
"modification_date": "2018-11-15",
"name": "Detect Prohibited Applications Spawning cmd.exe",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
detections/prohibited_software_on_endpoint.json
+151 -151
View File
@@ -1,158 +1,158 @@
{
"asset_type": "Endpoint",
"baselines": [
{
"id": "251930a5-1451-4428-bb13-eed5775be0ce",
"name": "Add Prohibited Processes to Enterprise Security",
"type": "splunk"
}
"asset_type": "Endpoint",
"baselines": [
{
"id": "251930a5-1451-4428-bb13-eed5775be0ce",
"name": "Add Prohibited Processes to Enterprise Security",
"type": "splunk"
}
],
"confidence": "high",
"creation_date": "2017-06-26",
"data_metadata": {
"data_models": [
"Endpoint"
],
"confidence": "high",
"creation_date": "2017-06-26",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search looks for applications on the endpoint that you have marked as prohibited.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, process, user",
"rule_description": "Prohibited software $process_name$ has been detected on $dest$.",
"rule_title": "Prohibited Software Detected On $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process | `ctime(firstTime)`| `ctime(lastTime)` | `drop_dm_object_name(Processes)` | `prohibited_softwares`",
"suppress": {
"suppress_fields": "dest,user",
"suppress_period": "86400s"
}
}
}
},
"eli5": "This search returns the number of times, as well as the first and last time, every process has run for each endpoint and user. It then displays only those processes that you have marked as \"prohibited\" in the Enterprise Security \"Interesting Processes\" table.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must be ingesting data that records 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 populated via Windows event logs, after enabling process tracking in your Windows audit settings. In addition, you must also have processes marked as \"prohibited\" in the Enterprise Security `interesting processes` table. To include the processes marked as \"prohibited\", which is included with ES Content Updates, run the included search `Support - Add Prohibited Processes to ES`.",
"id": "a51bfe1a-94f0-48cc-b4e4-b6ae50145893",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "This search looks for applications on the endpoint that you have marked as prohibited.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, process, user",
"rule_description": "Prohibited software $process_name$ has been detected on $dest$.",
"rule_title": "Prohibited Software Detected On $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process | `ctime(firstTime)`| `ctime(lastTime)` | `drop_dm_object_name(Processes)` | `prohibited_softwares`",
"suppress": {
"suppress_fields": "dest,user",
"suppress_period": "86400s"
}
}
}
{
"id": "d98675ed-da43-4a7e-96a7-eeca3232ba8e",
"name": "Get Update Logs For Endpoint",
"type": "splunk"
},
"eli5": "This search returns the number of times, as well as the first and last time, every process has run for each endpoint and user. It then displays only those processes that you have marked as \"prohibited\" in the Enterprise Security \"Interesting Processes\" table.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must be ingesting data that records 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 populated via Windows event logs, after enabling process tracking in your Windows audit settings. In addition, you must also have processes marked as \"prohibited\" in the Enterprise Security <code>interesting processes</code> table. To include the processes marked as \"prohibited\", which is included with ES Content Updates, run the included search <code>Support - Add Prohibited Processes to ES</code>.",
"id": "a51bfe1a-94f0-48cc-b4e4-b6ae50145893",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "d98675ed-da43-4a7e-96a7-eeca3232ba8e",
"name": "Get Update Logs For Endpoint",
"type": "splunk"
},
{
"id": "df7a7f50-30f2-4cde-8448-69d2d5f9b3c5",
"name": "Get Vulnerability Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd22",
"name": "Investigate Web Activity From Host",
"type": "splunk"
}
],
"known_false_positives": "None identified",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 2"
],
"kill_chain_phases": [
"Installation",
"Command and Control",
"Actions on Objectives"
],
"mitre_attack": [
"Execution"
],
"nist": [
"ID.AM",
"PR.DS"
]
{
"id": "df7a7f50-30f2-4cde-8448-69d2d5f9b3c5",
"name": "Get Vulnerability Logs For Endpoint",
"type": "splunk"
},
"modification_date": "2010-11-02",
"name": "Prohibited Software On Endpoint",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd22",
"name": "Investigate Web Activity From Host",
"type": "splunk"
}
],
"known_false_positives": "None identified",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 2"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
"kill_chain_phases": [
"Installation",
"Command and Control",
"Actions on Objectives"
],
"mitre_attack": [
"Execution"
],
"nist": [
"ID.AM",
"PR.DS"
]
},
"modification_date": "2010-11-02",
"name": "Prohibited Software On Endpoint",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/protocols_authenticating_in_cleartext.json
+122 -122
View File
@@ -1,127 +1,127 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2017-08-03",
"data_metadata": {
"data_models": [
"Network_Traffic"
],
"data_source": [
"Network Communications"
],
"providing_technologies": [
"Splunk Stream",
"Bro"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2017-08-03",
"data_metadata": {
"data_models": [
"Network_Traffic"
],
"data_source": [
"Network Communications"
],
"providing_technologies": [
"Splunk Stream",
"Bro"
]
},
"description": "This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src, dest, user",
"rule_description": "This search looks for the use of cleartext protocols that are known to pass authentication information in the clear. The cleartext credentials are typically passed at the beginning of the session.",
"rule_title": "Possible credential leak over cleartext protocol"
},
"risk": {
"risk_object": "user",
"risk_object_type": [
"user"
],
"risk_score": 60
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.protocol=\"tcp\" AND (All_Traffic.dest_port=\"23\" OR All_Traffic.dest_port=\"143\" OR All_Traffic.dest_port=\"110\" OR (All_Traffic.dest_port=\"21\" AND All_Traffic.user != \"anonymous\")) groupby All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name(\"All_Traffic\")`",
"suppress": {
"suppress_fields": "dest",
"suppress_period": "86400s"
}
}
}
},
"eli5": "This search is checking for traffic on well-known ports that are associated with protocols that pass authentication in cleartext.",
"entities": [
"user"
],
"how_to_implement": "This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model.",
"id": "6923cd64-17a0-453c-b945-81ac2d8c6db9",
"investigations": [
{
"id": "9925d08f-561e-4faa-8912-e3888a842341",
"name": "Get Process Information For Port Activity",
"type": "splunk"
},
"description": "This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src, dest, user",
"rule_description": "This search looks for the use of cleartext protocols that are known to pass authentication information in the clear. The cleartext credentials are typically passed at the beginning of the session.",
"rule_title": "Possible credential leak over cleartext protocol"
},
"risk": {
"risk_object": "user",
"risk_object_type": [
"user"
],
"risk_score": 60
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.protocol=\"tcp\" AND (All_Traffic.dest_port=\"23\" OR All_Traffic.dest_port=\"143\" OR All_Traffic.dest_port=\"110\" OR (All_Traffic.dest_port=\"21\" AND All_Traffic.user != \"anonymous\")) groupby All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name(\"All_Traffic\")`",
"suppress": {
"suppress_fields": "dest",
"suppress_period": "86400s"
}
}
}
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"eli5": "This search is checking for traffic on well-known ports that are associated with protocols that pass authentication in cleartext.",
"entities": [
"user"
],
"how_to_implement": "This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model.",
"id": "6923cd64-17a0-453c-b945-81ac2d8c6db9",
"investigations": [
{
"id": "9925d08f-561e-4faa-8912-e3888a842341",
"name": "Get Process Information For Port Activity",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Some networks may use kerberized FTP or telnet servers, however, this is rare.",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"mappings": {
"cis20": [
"CIS 9",
"CIS 14"
],
"kill_chain_phases": [
"Reconnaissance",
"Actions on Objectives"
],
"mitre_attack": [
"Credential Access",
"Lateral Movement",
"Collection"
],
"nist": [
"PR.PT",
"DE.AE",
"PR.AC",
"PR.DS"
]
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
"modification_date": "2017-09-15",
"name": "Protocols passing authentication in cleartext",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Some networks may use kerberized FTP or telnet servers, however, this is rare.",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"mappings": {
"cis20": [
"CIS 9",
"CIS 14"
],
"references": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
"kill_chain_phases": [
"Reconnaissance",
"Actions on Objectives"
],
"mitre_attack": [
"Credential Access",
"Lateral Movement",
"Collection"
],
"nist": [
"PR.PT",
"DE.AE",
"PR.AC",
"PR.DS"
]
},
"modification_date": "2017-09-15",
"name": "Protocols passing authentication in cleartext",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"references": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/psexec_accepteula.json
+125 -125
View File
@@ -1,131 +1,131 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2018-03-28",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Sysmon"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2018-03-28",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Sysmon"
]
},
"description": "This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest,process_name",
"rule_description": "The process pssxec.exe was run with the -accepteula flag on $dest$ by $user$.",
"rule_title": "PsExec executed with accepteula flag on $dest$."
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 75
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = PsExec.exe Processes.process = \"*accepteula*\" by Processes.process_name Processes.dest Processes.parent_process_name | `drop_dm_object_name(Processes)`| `ctime(firstTime)`| `ctime(lastTime)`",
"suppress": {
"suppress_fields": "dest, process_name",
"suppress_period": "86400s"
}
}
}
},
"eli5": "In this search, we are looking for the PsExec process with `accepteula` on the command line.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "b89919ed-fe5f-492c-b139-151xb162040e",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "This search looks for events where <code>PsExec.exe</code> is run with the <code>accepteula</code> 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 <code>accepteula</code> within the command line.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest,process_name",
"rule_description": "The process pssxec.exe was run with the -accepteula flag on $dest$ by $user$.",
"rule_title": "PsExec executed with accepteula flag on $dest$."
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 75
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = PsExec.exe Processes.process = \"*accepteula*\" by Processes.process_name Processes.dest Processes.parent_process_name | `drop_dm_object_name(Processes)`| `ctime(firstTime)`| `ctime(lastTime)`",
"suppress": {
"suppress_fields": "dest, process_name",
"suppress_period": "86400s"
}
}
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"eli5": "In this search, we are looking for the PsExec process with <code>accepteula</code> on the command line.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "b89919ed-fe5f-492c-b139-151xb162040e",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Administrators can leverage PsExec for accessing remote systems and might pass <code>accepteula</code> as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"Command-Line Interface"
],
"nist": [
"PR.PT",
"DE.CM"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"modification_date": "2019-02-26",
"name": "Detect PsExec With accepteula Flag",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"Command-Line Interface"
],
"nist": [
"PR.PT",
"DE.CM"
]
},
"modification_date": "2019-02-26",
"name": "Detect PsExec With accepteula Flag",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
detections/rare_executables_on_endpoint.json
+138 -138
View File
@@ -1,144 +1,144 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2016-08-09",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2016-08-09",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, process",
"rule_description": "The process $process$ was detected running on $dest. This process is rare in your environment.",
"rule_title": "Rare Process $process$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 20
},
"schedule": {
"cron_schedule": "10 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name | rename Processes.process_name as process | rex field=user \"(?<user_domain>.*)\\\\\\\\(?<user_name>.*)\" | `ctime(firstTime)`| `ctime(lastTime)`| search [| tstats count from datamodel=Endpoint.Processes by Processes.process_name | rare Processes.process_name limit=30 | rename Processes.process_name as process| `filter_rare_process_whitelist`| table process ]",
"suppress": {
"suppress_fields": "dest, process",
"suppress_period": "86400s"
}
}
}
},
"eli5": "This search first executes the subsearch and counts all of your processes to determine the 10 most rare (the limit set is 10). It then filters out whitelisted processes and outputs the first and last time a rare process was encountered, the destination where the process is running, the count of occurrences, and the users who initiated the processes.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must be ingesting data that records process activity from your hosts and populating the endpoint data model with the resultant dataset. The macro `filter_rare_process_whitelist` searches two lookup files to whitelist your processes. These consist of `rare_process_whitelist_default.csv` and `rare_process_whitelist_local.csv`. To add your own processes to the whitelist, add them to `rare_process_whitelist_local.csv`. If you wish to remove an entry from the default lookup file, you will have to modify the macro itself to set the whitelist value for that process to false. You can modify the limit parameter and search scheduling to better suit your environment.",
"id": "44fddcb2-8d3b-454c-874e-7c6de5a4f7ac",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, process",
"rule_description": "The process $process$ was detected running on $dest. This process is rare in your environment.",
"rule_title": "Rare Process $process$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 20
},
"schedule": {
"cron_schedule": "10 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name | rename Processes.process_name as process | rex field=user \"(?<user_domain>.*)\\\\\\\\(?<user_name>.*)\" | `ctime(firstTime)`| `ctime(lastTime)`| search [| tstats count from datamodel=Endpoint.Processes by Processes.process_name | rare Processes.process_name limit=30 | rename Processes.process_name as process| `filter_rare_process_whitelist`| table process ]",
"suppress": {
"suppress_fields": "dest, process",
"suppress_period": "86400s"
}
}
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"eli5": "This search first executes the subsearch and counts all of your processes to determine the 10 most rare (the limit set is 10). It then filters out whitelisted processes and outputs the first and last time a rare process was encountered, the destination where the process is running, the count of occurrences, and the users who initiated the processes.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must be ingesting data that records process activity from your hosts and populating the endpoint data model with the resultant dataset. The macro <code>filter_rare_process_whitelist</code> searches two lookup files to whitelist your processes. These consist of <code>rare_process_whitelist_default.csv</code> and <code>rare_process_whitelist_local.csv</code>. To add your own processes to the whitelist, add them to <code>rare_process_whitelist_local.csv</code>. If you wish to remove an entry from the default lookup file, you will have to modify the macro itself to set the whitelist value for that process to false. You can modify the limit parameter and search scheduling to better suit your environment.",
"id": "44fddcb2-8d3b-454c-874e-7c6de5a4f7ac",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd22",
"name": "Investigate Web Activity From Host",
"type": "splunk"
}
],
"known_false_positives": "Some legitimate processes may be only rarely executed in your environment. As these are identified, update <code>rare_process_whitelist_local.csv</code> to filter them out of your search results.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 2",
"CIS 8"
],
"kill_chain_phases": [
"Installation",
"Command and Control",
"Actions on Objectives"
],
"mitre_attack": [
"Execution"
],
"nist": [
"ID.AM",
"PR.PT",
"PR.DS",
"DE.CM"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"modification_date": "2018-10-30",
"name": "Detect Rare Executables",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd22",
"name": "Investigate Web Activity From Host",
"type": "splunk"
}
],
"known_false_positives": "Some legitimate processes may be only rarely executed in your environment. As these are identified, update `rare_process_whitelist_local.csv` to filter them out of your search results.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 2",
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "4.0"
"kill_chain_phases": [
"Installation",
"Command and Control",
"Actions on Objectives"
],
"mitre_attack": [
"Execution"
],
"nist": [
"ID.AM",
"PR.PT",
"PR.DS",
"DE.CM"
]
},
"modification_date": "2018-10-30",
"name": "Detect Rare Executables",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "4.0"
}
detections/reg_used_to_hide_files.json
+134 -134
View File
@@ -1,139 +1,139 @@
{
"asset_type": "",
"confidence": "medium",
"creation_date": "2017-10-27",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
"asset_type": "",
"confidence": "medium",
"creation_date": "2017-10-27",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "The search looks for command-line arguments used to hide a file or directory using the reg add command.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, process",
"rule_description": "Regedit.exe is used by attackers to hide malware files/directories in windows environments via registry key settings. This rule detects command-line arguments used to hide a file/directory",
"rule_title": "Regedit.exe used to hide a file/directory on $dest$ "
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = reg.exe Processes.process=\"*add*\" Processes.process=\"*Hidden*\" Processes.process=\"*REG_DWORD*\" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `ctime(firstTime)` |`ctime(lastTime)`| regex process = \"(/d\\s+2)\"",
"suppress": {
"suppress_fields": "dest,process",
"suppress_period": "86400s"
}
}
}
},
"eli5": "Reg.exe is a binary native to Windows platform used to edit the registry hives of the system. Attackers can leverage this binary to hide files by passing in arguments that are used to hide the files. In the search, we first gather results with keywords, add, Hidden, and REG_DWORD, that will be in the raw event and filter by process and the command-line. We then leverage regular expressions on the command-line field to look for /d value as 2 which is responsible for hiding a file or directory.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "c77162d3-f93c-45cc-80c8-22f6b5264x9f",
"investigations": [
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41xf9",
"name": "Get Registry Activities",
"type": "splunk"
},
"description": "The search looks for command-line arguments used to hide a file or directory using the reg add command.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, process",
"rule_description": "Regedit.exe is used by attackers to hide malware files/directories in windows environments via registry key settings. This rule detects command-line arguments used to hide a file/directory",
"rule_title": "Regedit.exe used to hide a file/directory on $dest$ "
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = reg.exe Processes.process=\"*add*\" Processes.process=\"*Hidden*\" Processes.process=\"*REG_DWORD*\" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `ctime(firstTime)` |`ctime(lastTime)`| regex process = \"(/d\\s+2)\"",
"suppress": {
"suppress_fields": "dest,process",
"suppress_period": "86400s"
}
}
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"eli5": "Reg.exe is a binary native to Windows platform used to edit the registry hives of the system. Attackers can leverage this binary to hide files by passing in arguments that are used to hide the files. In the search, we first gather results with keywords, add, Hidden, and REG_DWORD, that will be in the raw event and filter by process and the command-line. We then leverage regular expressions on the command-line field to look for /d value as 2 which is responsible for hiding a file or directory.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "c77162d3-f93c-45cc-80c8-22f6b5264x9f",
"investigations": [
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41xf9",
"name": "Get Registry Activities",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "None at the moment",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Defense Evasion",
"Persistence"
],
"nist": [
"DE.CM"
]
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"modification_date": "2019-02-27",
"name": "Reg.exe used to hide files/directories via registry keys",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "None at the moment",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Defense Evasion",
"Persistence"
],
"nist": [
"DE.CM"
]
},
"modification_date": "2019-02-27",
"name": "Reg.exe used to hide files/directories via registry keys",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
detections/remote_desktop_process_running_on_system.json
+137 -137
View File
@@ -1,143 +1,143 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2016-09-13",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2016-09-13",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user, process",
"rule_description": "The system $dest$ is running the remote desktop process, mstsc.exe. This system does not commonly run this application.",
"rule_title": "Remote Desktop Process Running On $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 40
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=mstsc.exe AND Processes.dest_category!=common_rdp_source by Processes.dest Processes.user Processes.process | `ctime(firstTime)`| `ctime(lastTime)` | `drop_dm_object_name(Processes)`",
"suppress": {
"suppress_fields": "dest,user",
"suppress_period": "28800s"
}
}
}
},
"eli5": "This search finds systems that do not commonly use remote desktop, but which begin using it. It filters out all systems that have the \"common_rdp_source\" category applied. Categories are applied to systems using the Assets and Identity framework.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. The search requires you to identify systems that do not commonly 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`.",
"id": "f5939373-8054-40ad-8c64-cec478a22a4a",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the <code>common_rdp_source category</code> in the Assets and Identity framework.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user, process",
"rule_description": "The system $dest$ is running the remote desktop process, mstsc.exe. This system does not commonly run this application.",
"rule_title": "Remote Desktop Process Running On $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 40
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=mstsc.exe AND Processes.dest_category!=common_rdp_source by Processes.dest Processes.user Processes.process | `ctime(firstTime)`| `ctime(lastTime)` | `drop_dm_object_name(Processes)`",
"suppress": {
"suppress_fields": "dest,user",
"suppress_period": "28800s"
}
}
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"eli5": "This search finds systems that do not commonly use remote desktop, but which begin using it. It filters out all systems that have the \"common_rdp_source\" category applied. Categories are applied to systems using the Assets and Identity framework.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. The search requires you to identify systems that do not commonly 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 <code>SA-IdentityManagement/lookups</code>.",
"id": "f5939373-8054-40ad-8c64-cec478a22a4a",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Remote Desktop may be used legitimately by users on the network.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
},
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 3",
"CIS 9",
"CIS 16"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Lateral Movement",
"Remote Desktop Protocol"
],
"nist": [
"DE.AE",
"PR.AC",
"PR.IP"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"modification_date": "2018-11-02",
"name": "Remote Desktop Process Running On System",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Remote Desktop may be used legitimately by users on the network.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
},
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 3",
"CIS 9",
"CIS 16"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "3.0"
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Lateral Movement",
"Remote Desktop Protocol"
],
"nist": [
"DE.AE",
"PR.AC",
"PR.IP"
]
},
"modification_date": "2018-11-02",
"name": "Remote Desktop Process Running On System",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "3.0"
}
detections/remote_registry_modifications.json
+133 -133
View File
@@ -1,139 +1,139 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2018-05-31",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2018-05-31",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon"
]
},
"description": "This search monitors for remote modifications to registry keys.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user",
"rule_description": "A registry key was modified remotely using the machine $dest$ by $user$.",
"rule_title": "Remote Registry Key Modification detection on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=\"\\\\\\\\*\" by Registry.dest , Registry.status, Registry.user | `ctime(lastTime)` | `ctime(firstTime)` | `drop_dm_object_name(Registry)`",
"suppress": {
"suppress_fields": "dest, user,registry_path",
"suppress_period": "14400s"
}
}
}
},
"eli5": "This search looks for modifications made to the Windows registry from remote locations using reg.exe&#151;a tool used to create/update/delete/modify Windows registry keys. It is accomplished through specifying the machine names in the registry path, by entering double backslashes, followed by a computer name. In this search, we look for registry changes where the registry path contains the name of a remote computer. The search returns the number of times the remote server has been accessed, the first and last times the activity occurred, the name of the modified registry path, the host on which the modification took place, and the name of the user that performed the modification.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must populate the `Change_Analysis` data model. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.",
"id": "c9f4b923-f8af-4155-b697-1354f5dcbc5e",
"investigations": [
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41xf9",
"name": "Get Registry Activities",
"type": "splunk"
},
"description": "This search monitors for remote modifications to registry keys.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user",
"rule_description": "A registry key was modified remotely using the machine $dest$ by $user$.",
"rule_title": "Remote Registry Key Modification detection on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=\"\\\\\\\\*\" by Registry.dest , Registry.status, Registry.user | `ctime(lastTime)` | `ctime(firstTime)` | `drop_dm_object_name(Registry)`",
"suppress": {
"suppress_fields": "dest, user,registry_path",
"suppress_period": "14400s"
}
}
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"eli5": "This search looks for modifications made to the Windows registry from remote locations using reg.exe&#151;a tool used to create/update/delete/modify Windows registry keys. It is accomplished through specifying the machine names in the registry path, by entering double backslashes, followed by a computer name. In this search, we look for registry changes where the registry path contains the name of a remote computer. The search returns the number of times the remote server has been accessed, the first and last times the activity occurred, the name of the modified registry path, the host on which the modification took place, and the name of the user that performed the modification.",
"entities": [
"dest"
],
"how_to_implement": "To successfully implement this search, you must populate the <code>Change_Analysis</code> data model. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.",
"id": "c9f4b923-f8af-4155-b697-1354f5dcbc5e",
"investigations": [
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41xf9",
"name": "Get Registry Activities",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Defense Evasion",
"Persistence",
"Lateral Movement"
],
"nist": [
"PR.PT",
"DE.CM"
]
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"modification_date": "2018-05-31",
"name": "Remote Registry Key modifications",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out.",
"maintainers": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Defense Evasion",
"Persistence",
"Lateral Movement"
],
"nist": [
"PR.PT",
"DE.CM"
]
},
"modification_date": "2018-05-31",
"name": "Remote Registry Key modifications",
"original_authors": [
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
detections/remote_wmi_command_attempt.json
+133 -133
View File
@@ -1,138 +1,138 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2017-01-13",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2017-01-13",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search looks for wmic.exe being launched with parameters to operate on remote systems.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest,user,process_name",
"rule_description": "This search looks for wmic.exe being launched with parameters to operate on remote systems.",
"rule_title": "Endpoint - Remote WMI command attempt"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "50 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe AND Processes.process= */node* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)`",
"suppress": {
"suppress_fields": "dest,user,process_name",
"suppress_period": "28800s"
}
}
}
},
"eli5": "Many a times, attackers leverage native Windows utilities that are designed to help administrators better manage their systems, infrastructure, and auditing, but are instead leveraged for malicious purposes. In this case, we are looking for instances of wmic.exe being run with various parameters that are not typically used by administrators.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "272df6de-61f1-4784-877c-1fbc3e2d0838",
"investigations": [
{
"id": "155e0571-7db6-42f2-aa62-9a3a4cf35c94",
"name": "Get Sysmon WMI Activity for Host",
"type": "splunk"
},
"description": "This search looks for wmic.exe being launched with parameters to operate on remote systems.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest,user,process_name",
"rule_description": "This search looks for wmic.exe being launched with parameters to operate on remote systems.",
"rule_title": "Endpoint - Remote WMI command attempt"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 30
},
"schedule": {
"cron_schedule": "50 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe AND Processes.process= */node* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)`",
"suppress": {
"suppress_fields": "dest,user,process_name",
"suppress_period": "28800s"
}
}
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"eli5": "Many a times, attackers leverage native Windows utilities that are designed to help administrators better manage their systems, infrastructure, and auditing, but are instead leveraged for malicious purposes. In this case, we are looking for instances of wmic.exe being run with various parameters that are not typically used by administrators.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "272df6de-61f1-4784-877c-1fbc3e2d0838",
"investigations": [
{
"id": "155e0571-7db6-42f2-aa62-9a3a4cf35c94",
"name": "Get Sysmon WMI Activity for Host",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Administrators may use this legitimately to gather info from remote systems.",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"mappings": {
"cis20": [
"CIS 3",
"CIS 5"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"Windows Management Instrumentation"
],
"nist": [
"PR.PT",
"PR.AT",
"PR.AC",
"PR.IP"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"modification_date": "2018-12-03",
"name": "Remote WMI Command Attempt",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Administrators may use this legitimately to gather info from remote systems.",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"mappings": {
"cis20": [
"CIS 3",
"CIS 5"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Execution",
"Windows Management Instrumentation"
],
"nist": [
"PR.PT",
"PR.AT",
"PR.AC",
"PR.IP"
]
},
"modification_date": "2018-12-03",
"name": "Remote WMI Command Attempt",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
detections/s3_access_from_new_ip.json
+1 -1
View File
@@ -49,7 +49,7 @@
}
}
},
"eli5": "Here the subsearch executes first and returns all successful S3 bucket-access attempts (HTTP code \"200\") within the last hour. It groups the results by the earliest and latest times it has seen a remote IP accessing a particular bucket. It appends this information to the historical data from the lookup file and then recalculates the <code>firstTime</code> and <code>lastTime</code> 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 ",
"eli5": "Here the subsearch executes first and returns all successful S3 bucket-access attempts (HTTP code \"200\") within the last hour. It groups the results by the earliest and latest times it has seen a remote IP accessing a particular bucket. It appends this information to the historical data from the lookup file and then recalculates the `firstTime` and `lastTime` field for each remote IP accessing an S3 bucket. Next, it returns only those remote IP addresses that have first been seen accessing a specific bucket within the past hour. This is combined with the main search to return the time, bucket name, source IP, city, and country operations performed, as well as the requested URI of the resource ",
"entities": [
"src_ip"
],
detections/shim_database_file_creation.json
+132 -132
View File
@@ -1,137 +1,137 @@
{
"asset_type": "Endpoint",
"confidence": "high",
"creation_date": "2017-10-03",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon"
]
"asset_type": "Endpoint",
"confidence": "high",
"creation_date": "2017-10-03",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon"
]
},
"description": "This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, file_name",
"rule_description": "A file, $file_name$, was created in the default shim database directory on $dest.",
"rule_title": "Shim database file created on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 20
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Filesystem.action) values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\\AppPatch\\Custom* by Filesystem.file_name Filesystem.dest | `ctime(lastTime)` | `ctime(firstTime)` |`drop_dm_object_name(Filesystem)`",
"suppress": {
"suppress_fields": "dest",
"suppress_period": "14400s"
}
}
}
},
"eli5": "This search looks for files being created in `Windows\\AppPatch\\Custom and Windows\\AppPatch\\Custom64`, the location where shim databases are installed. It will return all the files created, as well as the time of creation for the first and last file for each endpoint.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.",
"id": "6e4c4588-ba2f-42fa-97e6-9f6f548eaa33",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, file_name",
"rule_description": "A file, $file_name$, was created in the default shim database directory on $dest.",
"rule_title": "Shim database file created on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 20
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(Filesystem.action) values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\\AppPatch\\Custom* by Filesystem.file_name Filesystem.dest | `ctime(lastTime)` | `ctime(firstTime)` |`drop_dm_object_name(Filesystem)`",
"suppress": {
"suppress_fields": "dest",
"suppress_period": "14400s"
}
}
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"eli5": "This search looks for files being created in <code>Windows\\AppPatch\\Custom and Windows\\AppPatch\\Custom64</code>, the location where shim databases are installed. It will return all the files created, as well as the time of creation for the first and last file for each endpoint.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.",
"id": "6e4c4588-ba2f-42fa-97e6-9f6f548eaa33",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Because legitimate shim files are created and used all the time, this event, in itself, is not suspicious. However, if there are other correlating events, it may warrant further investigation.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
},
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Persistence",
"Application Shimming"
],
"nist": [
"DE.CM"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"modification_date": "2018-11-02",
"name": "Shim Database File Creation",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "Because legitimate shim files are created and used all the time, this event, in itself, is not suspicious. However, if there are other correlating events, it may warrant further investigation.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
},
{
"company": "Splunk",
"email": "bpatel@splunk.com",
"name": "Bhavin Patel"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Persistence",
"Application Shimming"
],
"nist": [
"DE.CM"
]
},
"modification_date": "2018-11-02",
"name": "Shim Database File Creation",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
detections/shim_database_installation.json
+129 -129
View File
@@ -1,134 +1,134 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2017-10-03",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2017-10-03",
"data_metadata": {
"data_models": [
"Endpoint"
],
"data_source": [
"Endpoint Intel"
],
"providing_technologies": [
"Carbon Black Response",
"CrowdStrike Falcon",
"Sysmon",
"Tanium",
"Ziften"
]
},
"description": "This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user, process",
"rule_description": "The system $dest$ had a shim database installed.",
"rule_title": "Shim Database Installation on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 20
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe Processes.process=\"*-p*\" Processes.process=\"*-q*\" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `ctime(firstTime)` | `ctime(lastTime)`",
"suppress": {
"suppress_fields": "dest,user",
"suppress_period": "14400s"
}
}
}
},
"eli5": "This search looks for the execution of sdbinst.exe with command-line arguments of -q and -p. The -q option performs a silent installation with no visible window, status, or warning information. The -p option allows the shim database to contain patches. It will return the count, the first time, and the last time these command-line arguments were seen on each endpoint and by each user.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "404620de-46d8-48b6-90cc-8a8d7b0876a3",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"description": "This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "dest, user, process",
"rule_description": "The system $dest$ had a shim database installed.",
"rule_title": "Shim Database Installation on $dest$"
},
"risk": {
"risk_object": "dest",
"risk_object_type": [
"system"
],
"risk_score": 20
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe Processes.process=\"*-p*\" Processes.process=\"*-q*\" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `ctime(firstTime)` | `ctime(lastTime)`",
"suppress": {
"suppress_fields": "dest,user",
"suppress_period": "14400s"
}
}
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
"eli5": "This search looks for the execution of sdbinst.exe with command-line arguments of -q and -p. The -q option performs a silent installation with no visible window, status, or warning information. The -p option allows the shim database to contain patches. It will return the count, the first time, and the last time these command-line arguments were seen on each endpoint and by each user.",
"entities": [
"dest"
],
"how_to_implement": "You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the \"process\" field in the Endpoint data model.",
"id": "404620de-46d8-48b6-90cc-8a8d7b0876a3",
"investigations": [
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "None identified",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Persistence",
"Application Shimming"
],
"nist": [
"DE.CM"
]
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
"modification_date": "2019-03-01",
"name": "Shim Database Installation With Suspicious Parameters",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "None identified",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Persistence",
"Application Shimming"
],
"nist": [
"DE.CM"
]
},
"modification_date": "2019-03-01",
"name": "Shim Database Installation With Suspicious Parameters",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"references": [],
"security_domain": "endpoint",
"spec_version": 2,
"type": "splunk",
"version": "2.0"
}
detections/smb_traffic_spike.json
+137 -137
View File
@@ -1,143 +1,143 @@
{
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2017-08-20",
"data_metadata": {
"data_models": [
"Network_Traffic"
],
"data_source": [
"Network Communications"
],
"providing_technologies": [
"Bro",
"Splunk Stream"
]
"asset_type": "Endpoint",
"confidence": "medium",
"creation_date": "2017-08-20",
"data_metadata": {
"data_models": [
"Network_Traffic"
],
"data_source": [
"Network Communications"
],
"providing_technologies": [
"Bro",
"Splunk Stream"
]
},
"description": "This search looks for spikes in the number of Server Message Block (SMB) traffic connections.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src",
"rule_description": "There was a spike in SMB traffic from $src$.",
"rule_title": "SMB Traffic Spike from $src$"
},
"risk": {
"risk_object": "src",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-7d@d",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | `drop_dm_object_name(\"All_Traffic\")` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, \"-70m@m\"), count, null))) as count avg(eval(if(_time<relative_time(maxtime, \"-70m@m\"), count, null))) as avg stdev(eval(if(_time<relative_time(maxtime, \"-70m@m\"), count, null))) as stdev by src | eval upperBound=(avg+stdev*2), isOutlier=if(count > upperBound AND num_data_samples >=50, 1, 0) | where isOutlier=1 | table src count",
"suppress": {
"suppress_fields": "src",
"suppress_period": "28800s"
}
}
}
},
"eli5": "Server Message Block (SMB) traffic, a protocol used for Windows file sharing-activity, is often leveraged by attackers. One example of SMB abuse was the WannaCry ransomware, which leveraged a vulnerability in the SMB protocol to propagate to other systems. Attackers have also used SMB for lateral movement with a target environment and to test credentials against target systems. While SMB is highly prevalent in Windows environments, a spike in SMB traffic may still be indicative of this type of malicious activity. This search looks for a traffic spike in SMB traffic from a particular system. If such a spike is detected, you may want to investigate the source and analyze the cause of the abnormal traffic.",
"entities": [
"src"
],
"how_to_implement": "This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model.",
"id": "7f5fb3e1-4209-4914-90db-0ec21b936378",
"investigations": [
{
"id": "9925d08f-561e-4faa-8912-e3888a842341",
"name": "Get Process Information For Port Activity",
"type": "splunk"
},
"description": "This search looks for spikes in the number of Server Message Block (SMB) traffic connections.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src",
"rule_description": "There was a spike in SMB traffic from $src$.",
"rule_title": "SMB Traffic Spike from $src$"
},
"risk": {
"risk_object": "src",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-7d@d",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | `drop_dm_object_name(\"All_Traffic\")` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, \"-70m@m\"), count, null))) as count avg(eval(if(_time<relative_time(maxtime, \"-70m@m\"), count, null))) as avg stdev(eval(if(_time<relative_time(maxtime, \"-70m@m\"), count, null))) as stdev by src | eval upperBound=(avg+stdev*2), isOutlier=if(count > upperBound AND num_data_samples >=50, 1, 0) | where isOutlier=1 | table src count",
"suppress": {
"suppress_fields": "src",
"suppress_period": "28800s"
}
}
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"eli5": "Server Message Block (SMB) traffic, a protocol used for Windows file sharing-activity, is often leveraged by attackers. One example of SMB abuse was the WannaCry ransomware, which leveraged a vulnerability in the SMB protocol to propagate to other systems. Attackers have also used SMB for lateral movement with a target environment and to test credentials against target systems. While SMB is highly prevalent in Windows environments, a spike in SMB traffic may still be indicative of this type of malicious activity. This search looks for a traffic spike in SMB traffic from a particular system. If such a spike is detected, you may want to investigate the source and analyze the cause of the abnormal traffic.",
"entities": [
"src"
],
"how_to_implement": "This search requires you to be ingesting your network traffic logs and populating the <code>Network_Traffic</code> data model.",
"id": "7f5fb3e1-4209-4914-90db-0ec21b936378",
"investigations": [
{
"id": "9925d08f-561e-4faa-8912-e3888a842341",
"name": "Get Process Information For Port Activity",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "df7a7f50-30f2-4cde-8448-69d2d5f9b3c5",
"name": "Get Vulnerability Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "A file server may experience high-demand loads that could cause this analytic to trigger.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Lateral Movement",
"Execution",
"Command and Control",
"Commonly Used Port"
],
"nist": [
"DE.CM"
]
{
"id": "df7a7f50-30f2-4cde-8448-69d2d5f9b3c5",
"name": "Get Vulnerability Logs For Endpoint",
"type": "splunk"
},
"modification_date": "2017-09-10",
"name": "SMB Traffic Spike",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "A file server may experience high-demand loads that could cause this analytic to trigger.",
"maintainers": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"references": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Lateral Movement",
"Execution",
"Command and Control",
"Commonly Used Port"
],
"nist": [
"DE.CM"
]
},
"modification_date": "2017-09-10",
"name": "SMB Traffic Spike",
"original_authors": [
{
"company": "Splunk",
"email": "davidd@splunk.com",
"name": "David Dorsey"
}
],
"references": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}
detections/smb_traffic_spike_mltk.json
+143 -143
View File
@@ -1,150 +1,150 @@
{
"asset_type": "Endpoint",
"baselines": [
{
"id": "df98763b-0b08-4281-8ef9-08db7ac572a9",
"name": "Baseline of SMB Traffic - MLTK",
"type": "splunk"
}
"asset_type": "Endpoint",
"baselines": [
{
"id": "df98763b-0b08-4281-8ef9-08db7ac572a9",
"name": "Baseline of SMB Traffic - MLTK",
"type": "splunk"
}
],
"confidence": "medium",
"creation_date": "2019-05-08",
"data_metadata": {
"data_models": [
"Network_Traffic"
],
"confidence": "medium",
"creation_date": "2019-05-08",
"data_metadata": {
"data_models": [
"Network_Traffic"
],
"data_source": [
"Network Communications"
],
"providing_technologies": [
"Bro",
"Splunk Stream"
]
"data_source": [
"Network Communications"
],
"providing_technologies": [
"Bro",
"Splunk Stream"
]
},
"description": "This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src",
"rule_description": "There was a spike in SMB traffic from $src$",
"rule_title": "SMB Traffic Spike from $src$"
},
"risk": {
"risk_object": "src",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | eval HourOfDay=strftime(_time, \"%H\") | eval DayOfWeek=strftime(_time, \"%A\") | `drop_dm_object_name(All_Traffic)` | apply smb_pdfmodel threshold=0.001 | rename \"IsOutlier(count)\" as isOutlier | search isOutlier > 0 | sort -count | table _time src dest port count",
"suppress": {
"suppress_fields": "src",
"suppress_period": "28800s"
}
}
}
},
"eli5": "Attackers often leverage Server Message Block (SMB) traffic, a protocol used for Windows file-sharing activity. A high-profile example of SMB abuse was the WannaCry ransomware, which leveraged a vulnerability in the SMB protocol to propagate to other systems. Attackers have also used SMB for lateral movement with a target environment and to test credentials against target systems. While SMB is highly prevalent in Windows environments, a spike in SMB traffic may still be indicative of this type of malicious activity. This search leverages Splunk's Machine Learning Toolkit (MLTK) to identify spikes in SMB traffic that are unusual for a given hour of day/day of week combination. If such a spike is detected, you may want to investigate the source and analyze the cause of the abnormal traffic. The determination of what is considered an outlier may be adjusted via the threshold parameter in the search. More information on the algorithm used can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.",
"entities": [
"src"
],
"how_to_implement": "To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search \"Baseline of SMB Traffic - MLTK\" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\n\nThis 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): **Label:** Number of events, **Field:** count\n\nDetailed documentation on how to create a new field within Incident Review is found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`",
"id": "d25773ba-9ad8-48d1-858e-07ad0bbeb828",
"investigations": [
{
"id": "9925d08f-561e-4faa-8912-e3888a842341",
"name": "Get Process Information For Port Activity",
"type": "splunk"
},
"description": "This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections.",
"detect": {
"splunk": {
"correlation_rule": {
"notable": {
"nes_fields": "src",
"rule_description": "There was a spike in SMB traffic from $src$",
"rule_title": "SMB Traffic Spike from $src$"
},
"risk": {
"risk_object": "src",
"risk_object_type": [
"system"
],
"risk_score": 50
},
"schedule": {
"cron_schedule": "0 * * * *",
"earliest_time": "-70m@m",
"latest_time": "-10m@m"
},
"search": "| tstats `summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | eval HourOfDay=strftime(_time, \"%H\") | eval DayOfWeek=strftime(_time, \"%A\") | `drop_dm_object_name(All_Traffic)` | apply smb_pdfmodel threshold=0.001 | rename \"IsOutlier(count)\" as isOutlier | search isOutlier > 0 | sort -count | table _time src dest port count",
"suppress": {
"suppress_fields": "src",
"suppress_period": "28800s"
}
}
}
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
"eli5": "Attackers often leverage Server Message Block (SMB) traffic, a protocol used for Windows file-sharing activity. A high-profile example of SMB abuse was the WannaCry ransomware, which leveraged a vulnerability in the SMB protocol to propagate to other systems. Attackers have also used SMB for lateral movement with a target environment and to test credentials against target systems. While SMB is highly prevalent in Windows environments, a spike in SMB traffic may still be indicative of this type of malicious activity. This search leverages Splunk's Machine Learning Toolkit (MLTK) to identify spikes in SMB traffic that are unusual for a given hour of day/day of week combination. If such a spike is detected, you may want to investigate the source and analyze the cause of the abnormal traffic. The determination of what is considered an outlier may be adjusted via the threshold parameter in the search. More information on the algorithm used can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.",
"entities": [
"src"
],
"how_to_implement": "To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search \"Baseline of SMB Traffic - MLTK\" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.<br></br>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): <b>Label:</b> Number of events, <b>Field:</b> count<br></br>Detailed documentation on how to create a new field within Incident Review is found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`",
"id": "d25773ba-9ad8-48d1-858e-07ad0bbeb828",
"investigations": [
{
"id": "9925d08f-561e-4faa-8912-e3888a842341",
"name": "Get Process Information For Port Activity",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd76",
"name": "Get Authentication Logs For Endpoint",
"type": "splunk"
},
{
"id": "df7a7f50-30f2-4cde-8448-69d2d5f9b3c5",
"name": "Get Vulnerability Logs For Endpoint",
"type": "splunk"
},
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data.",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Lateral Movement",
"Execution",
"Command and Control",
"Commonly Used Port"
],
"nist": [
"DE.CM"
]
{
"id": "df7a7f50-30f2-4cde-8448-69d2d5f9b3c5",
"name": "Get Vulnerability Logs For Endpoint",
"type": "splunk"
},
"modification_date": "2019-05-08",
"name": "SMB Traffic Spike - MLTK",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
{
"id": "fecf2918-670d-4f1c-872b-3d7317a41bf9",
"name": "Get Parent Process Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d55",
"name": "Get Risk Modifiers For User",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd71",
"name": "Get Process Info",
"type": "splunk"
},
{
"id": "3d6c3213-5fff-4a1e-b57d-b24c262171e7",
"name": "Get Notable History",
"type": "splunk"
},
{
"id": "f3fb4d1b-5f33-4b01-b541-c7af9534c242",
"name": "Get Notable Info",
"type": "splunk"
},
{
"id": "fdcfb369-1725-4c24-824a-22972d7f0d65",
"name": "Get Risk Modifiers For Endpoint",
"type": "splunk"
},
{
"id": "bc91a8cf-35e7-4bb2-8140-e756cc06fd74",
"name": "Get User Information from Identity Table",
"type": "splunk"
}
],
"known_false_positives": "If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data.",
"maintainers": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"mappings": {
"cis20": [
"CIS 8"
],
"references": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
"kill_chain_phases": [
"Actions on Objectives"
],
"mitre_attack": [
"Lateral Movement",
"Execution",
"Command and Control",
"Commonly Used Port"
],
"nist": [
"DE.CM"
]
},
"modification_date": "2019-05-08",
"name": "SMB Traffic Spike - MLTK",
"original_authors": [
{
"company": "Splunk",
"email": "rvaldez@splunk.com",
"name": "Rico Valdez"
}
],
"references": [],
"security_domain": "network",
"spec_version": 2,
"type": "splunk",
"version": "1.0"
}

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