Linux Auditd Osquery Service Stop
Description
The following analytic detects suspicious stopping of the osquery
service, which may indicate an attempt to disable monitoring and evade detection. Osquery
is a powerful tool used for querying system information and detecting anomalies, and stopping its service can be a sign that an attacker is trying to disrupt security monitoring or hide malicious activities. By monitoring for unusual or unauthorized stops of the osquery
service, this analytic helps identify potential efforts to bypass security controls, enabling security teams to investigate and respond to possible threats effectively.
- Type: TTP
-
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2024-09-04
- Author: Teoderick Contreras, Splunk
- ID: 0c320fea-6e87-4b99-a884-74d09d4b655d
Annotations
Kill Chain Phase
- Actions On Objectives
NIST
- DE.CM
CIS20
- CIS 10
CVE
Search
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`linux_auditd` type=SERVICE_STOP unit IN ("osqueryd")
| rename host as dest
| stats count min(_time) as firstTime max(_time) as lastTime by type pid UID comm exe unit dest
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `linux_auditd_osquery_service_stop_filter`
Macros
The SPL above uses the following Macros:
linux_auditd_osquery_service_stop_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
- Linux Living Off The Land
- Linux Privilege Escalation
- Linux Persistence Techniques
- Compromised Linux Host
RBA
Risk Score | Impact | Confidence | Message |
---|---|---|---|
64.0 | 80 | 80 | A service event - [$type$] event occured on host - [$dest$] to stop the osquery service. |
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
source | version: 1