Detection: Linux Auditd Virtual Disk File And Directory Discovery

Description

The following analytic detects suspicious discovery of virtual disk files and directories, which may indicate an attacker's attempt to locate and access virtualized storage environments. Virtual disks can contain sensitive data or critical system configurations, and unauthorized discovery attempts could signify preparatory actions for data exfiltration or further compromise. By monitoring for unusual or unauthorized searches for virtual disk files and directories, this analytic helps identify potential reconnaissance activities, enabling security teams to respond promptly and safeguard against unauthorized access and data breaches.

Search

1`linux_auditd` `linux_auditd_normalized_execve_process` 
2| rename host as dest 
3| where  (LIKE (process_exec, "%find%") OR LIKE (process_exec, "%grep%")) AND (LIKE (process_exec, "%.vhd%") OR LIKE (process_exec, "%.vhdx%") OR LIKE (process_exec, "%.vmdk%")) 
4| stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest 
5| `security_content_ctime(firstTime)` 
6| `security_content_ctime(lastTime)`
7| `linux_auditd_virtual_disk_file_and_directory_discovery_filter`

Data Source

Name	Platform	Sourcetype	Source
Linux Auditd Execve	Linux	'linux:audit'	'/var/log/audit/audit.log'

Macros Used

Annotations

Default Configuration

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

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

Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.

Associated Analytic Story

• Compromised Linux Host

• Linux Living Off The Land

• Linux Persistence Techniques

• Linux Privilege Escalation

Risk Based Analytics (RBA)

References

• https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html

• https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS

Detection Testing

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: 2