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Description

The following analytic detects attempts to stop or clear a service on Linux systems. It leverages data from Linux Auditd, focusing on processes like "systemctl," "service," and "svcadm" executing stop commands. This activity is significant as adversaries often terminate security or critical services to disable defenses or disrupt operations, as seen in malware like Industroyer2. If confirmed malicious, this could lead to the disabling of security mechanisms, allowing attackers to persist, escalate privileges, or deploy destructive payloads, severely impacting system integrity and availability.

  • Type: TTP

  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

  • Last Updated: 2024-09-04
  • Author: Teoderick Contreras, Splunk
  • ID: 43bc9281-753b-4743-b4b7-60af84f085f3

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1489 Service Stop Impact
Kill Chain Phase
  • Actions On Objectives
NIST
  • DE.CM
CIS20
  • CIS 10
CVE 


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`linux_auditd` type=SERVICE_STOP 
| rename host as dest 
| stats count min(_time) as firstTime max(_time) as lastTime by type pid UID comm exe dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `linux_auditd_stop_services_filter`

Macros

The SPL above uses the following Macros:

:information_source: linux_auditd_stop_services_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
  • type
  • pid
  • UID
  • comm
  • exe

How To Implement

To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed

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

RBA

Risk Score Impact Confidence Message
49.0 70 70 A service event - [$type$] event occured on host - [$dest$] to stop or disable a service.

: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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