Detection: Linux Auditd Hidden Files And Directories Creation

Description

The following analytic detects suspicious creation of hidden files and directories, which may indicate an attacker's attempt to conceal malicious activities or unauthorized data. Hidden files and directories are often used to evade detection by security tools and administrators, providing a stealthy means for storing malware, logs, or sensitive information. By monitoring for unusual or unauthorized creation of hidden files and directories, this analytic helps identify potential attempts to hide or unauthorized creation of hidden files and directories, this analytic helps identify potential attempts to hide malicious operations, enabling security teams to uncover and address hidden threats effectively.

Search

1`linux_auditd` `linux_auditd_normalized_execve_process` 
2| rename host as dest 
3| where (LIKE (process_exec,"%touch %") OR LIKE (process_exec,"%mkdir %")OR LIKE (process_exec,"%vim %") OR LIKE (process_exec,"%vi %") OR LIKE (process_exec,"%nano %")) AND (LIKE (process_exec,"% ./.%")  OR LIKE (process_exec," .%")OR LIKE (process_exec," /.%")) 
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_hidden_files_and_directories_creation_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: 3