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Description

The following analytic detects the restarting or re-enabling of services in the Linux platform. It focuses on the use of the systemctl or service tools for executing these actions. Adversaries may leverage this technique to repeatedly execute malicious payloads as a form of persistence. Linux hosts typically start services during boot to perform background system functions. However, administrators may also create legitimate services for specific tools or applications as part of task automation. In such cases, it is recommended to verify the service path of the registered script or executable and identify the creator of the service for further validation.
It's important to be aware that this analytic may generate false positives as administrators or network operators may use the same command-line for legitimate automation purposes. Filter macros should be updated accordingly to minimize false positives.
Identifying restarted or re-enabled services is valuable for a SOC as it can indicate potential malicious activities attempting to maintain persistence or execute unauthorized actions on Linux systems. By detecting and investigating these events, security analysts can respond promptly to mitigate risks and prevent further compromise. The impact of a true positive can range from unauthorized access to data destruction or other damaging outcomes.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Endpoint
  • Last Updated: 2023-04-14
  • Author: Teoderick Contreras, Splunk
  • ID: 084275ba-61b8-11ec-8d64-acde48001122

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1053.006 Systemd Timers Execution, Persistence, Privilege Escalation
T1053 Scheduled Task/Job Execution, Persistence, Privilege Escalation
Kill Chain Phase
  • Installation
  • Exploitation
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name IN ("systemctl", "service") OR Processes.process IN ("*systemctl *", "*service *")) Processes.process IN ("*restart*", "*reload*", "*reenable*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `linux_service_restarted_filter`

Macros

The SPL above uses the following Macros:

:information_source: linux_service_restarted_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Required fields

List of fields required to use this analytic.

  • _time
  • Processes.dest
  • Processes.user
  • Processes.parent_process_name
  • Processes.process_name
  • Processes.process
  • Processes.process_id
  • Processes.parent_process_id

How To Implement

The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the Processes node of the Endpoint data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.

Known False Positives

Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
25.0 50 50 A commandline $process$ that may create or start a service on $dest$

:information_source: The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

Reference

Test Dataset

Replay any dataset to Splunk Enterprise by using our replay.py tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

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