Linux Auditd Stop Services

SPLUNK detection rule by Teoderick Contreras, Splunk · Apache-2.0

The following analytic detects attempts to stop a service on Linux systems. It leverages data from Linux Auditd. This activity is significant as adversaries often stop or 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.

name: Linux Auditd Stop Services
id: 43bc9281-753b-4743-b4b7-60af84f085f3
version: 9
creation_date: '2024-08-12'
modification_date: '2026-05-13'
author: Teoderick Contreras, Splunk
status: production
type: Hunting
description: The following analytic detects attempts to stop a service on Linux systems. It leverages data from Linux Auditd. This activity is significant as adversaries often stop or 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.
data_source:
    - Linux Auditd Service Stop
search: |-
    `linux_auditd` type=SERVICE_STOP
      | rename host as dest
      | stats count min(_time) as firstTime max(_time) as lastTime
        BY type pid comm
           exe dest
      | `security_content_ctime(firstTime)`
      | `security_content_ctime(lastTime)`
      | `linux_auditd_stop_services_filter`
how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of 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.
references:
    - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/
    - https://cert.gov.ua/article/39518
drilldown_searches:
    - name: View the detection results for - "$dest$"
      search: '%original_detection_search% | search  dest = "$dest$"'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
    - name: View risk events for the last 7 days for - "$dest$"
      search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
      earliest_offset: 7d
      latest_offset: "0"
analytic_story:
    - Industroyer2
    - Data Destruction
    - AwfulShred
    - Compromised Linux Host
asset_type: Endpoint
mitre_attack_id:
    - T1489
product:
    - Splunk Enterprise
    - Splunk Enterprise Security
    - Splunk Cloud
category: endpoint
security_domain: endpoint
tests:
    - name: True Positive Test
      attack_data:
        - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_auditd_service_stop/linux_auditd_service_stop.log
          source: auditd
          sourcetype: auditd
      test_type: unit

[[ currentRule.title && currentRule.title.length > 60 ? currentRule.title.slice(0, 60) + '…' : currentRule.title ]]

17a41682-ae0d-455e-83a3-4f188809534c

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Executable code, not a declarative rule

This submission is arbitrary [[ currentRule.format ? currentRule.format.toUpperCase() : '' ]] code, not a declarative detection rule — review it as you would any script before running it, and note that it cannot be safely aggregated or auto-executed alongside YARA/Sigma/Suricata/CRS rules.

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Format & documentation suggestions

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