Detection: Linux Auditd Kernel Module Enumeration

Updated Date: 2024-09-04 ID: d1b088de-c47a-4572-9339-bdcc26493b32 Author: Teoderick Contreras, Splunk Type: Anomaly Product: Splunk Enterprise Security

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.

1`linux_auditd` type=SYSCALL comm=lsmod 
2| rename host as dest  
3| stats count min(_time) as firstTime max(_time) as lastTime by comm exe  SYSCALL UID ppid pid success dest 
4| `security_content_ctime(firstTime)`
5| `security_content_ctime(lastTime)` 
6| `linux_auditd_kernel_module_enumeration_filter`

Data Source

Name Platform Sourcetype Source Supported App
Linux Auditd Syscall Linux icon Linux 'linux:audit' '/var/log/audit/audit.log' N/A

Macros Used

Name Value
linux_auditd sourcetype="linux:audit"
linux_auditd_kernel_module_enumeration_filter search *
linux_auditd_kernel_module_enumeration_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Annotations

- MITRE ATT&CK
+ Kill Chain Phases
+ NIST
+ CIS
- Threat Actors
ID Technique Tactic
T1082 System Information Discovery Discovery
T1014 Rootkit Defense Evasion
KillChainPhase.EXPLOITAITON
NistCategory.DE_AE
Cis18Value.CIS_10
APT18
APT19
APT3
APT32
APT37
APT38
APT41
Aquatic Panda
Blue Mockingbird
Chimera
Confucius
Darkhotel
FIN13
FIN8
Gamaredon Group
HEXANE
Higaisa
Inception
Ke3chang
Kimsuky
Lazarus Group
Magic Hound
Malteiro
Moses Staff
MuddyWater
Mustang Panda
Mustard Tempest
OilRig
Patchwork
Rocke
Sandworm Team
SideCopy
Sidewinder
Sowbug
Stealth Falcon
TA2541
TeamTNT
ToddyCat
Tropic Trooper
Turla
Volt Typhoon
Windigo
Windshift
Wizard Spider
ZIRCONIUM
admin@338
APT28
APT41
Rocke
TeamTNT
Winnti Group

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Setting Value
Disabled true
Cron Schedule 0 * * * *
Earliest Time -70m@m
Latest Time -10m@m
Schedule Window auto
Creates Risk Event True
This configuration file applies to all detections of type anomaly. These detections will use Risk Based Alerting.

Implementation

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

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
A SYSCALL - [$comm$] event was executed on host - [$dest$] to list kernel modules. 15 30 50
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

References

Detection Testing

Test Type Status Dataset Source Sourcetype
Validation ✅ Passing N/A N/A N/A
Unit ✅ Passing Dataset /var/log/audit/audit.log linux:audit
Integration ✅ Passing Dataset /var/log/audit/audit.log linux:audit

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: GitHub | Version: 1