Linux Auditd Kernel Module Enumeration
Description
The following analytic identifies the use of the 'kmod' process to list kernel modules on a Linux system. This detection leverages data from Linux Auditd, focusing on process names and command-line executions. While listing kernel modules is not inherently malicious, it can be a precursor to loading unauthorized modules using 'insmod'. If confirmed malicious, this activity could allow an attacker to load kernel modules, potentially leading to privilege escalation, persistence, or other malicious actions within the system.
- Type: Anomaly
-
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2024-09-04
- Author: Teoderick Contreras, Splunk
- ID: d1b088de-c47a-4572-9339-bdcc26493b32
Annotations
ATT&CK
Kill Chain Phase
- Exploitation
NIST
- DE.AE
CIS20
- CIS 10
CVE
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`linux_auditd` type=SYSCALL comm=lsmod
| rename host as dest
| stats count min(_time) as firstTime max(_time) as lastTime by comm exe SYSCALL UID ppid pid success dest
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `linux_auditd_kernel_module_enumeration_filter`
Macros
The SPL above uses the following Macros:
linux_auditd_kernel_module_enumeration_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
- comm
- exe
- SYSCALL
- UID
- ppid
- pid
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
False positives are present based on automated tooling or system administrative usage. Filter as needed.
Associated Analytic Story
RBA
Risk Score | Impact | Confidence | Message |
---|---|---|---|
15.0 | 30 | 50 | A SYSCALL - [$comm$] event was executed on host - [$dest$] to list kernel modules. |
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