The following analytic is to detect events that attempts to disable a service. This is typically identified in parallel with other instances of service enumeration of attempts to stop a service and then delete it. Adversaries utilize this technique like industroyer2 malware to terminate security services or other related services to continue there objective as a destructive payload.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2022-04-22
- Author: Teoderick Contreras, Splunk
- ID: f2e08a38-6689-4df4-ad8c-b51c16262316
Kill Chain Phase
- CIS 3
- CIS 5
- CIS 16
1 2 3 4 5 6 | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("systemctl", "service", "svcadm") Processes.process = "* disable*" by Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_disable_services_filter`
The SPL above uses the following Macros:
linux_disable_services_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
How To Implement
To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
Known False Positives
Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
Associated Analytic story
|49.0||70||70||An instance of $parent_process_name$ spawning $process_name$ was identified attempting to disable services on endpoint $dest$ by $user$.|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
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