Detection: Linux Auditd Data Transfer Size Limits Via Split Syscall

Description

The following analytic detects suspicious data transfer activities that involve the use of the split syscall, potentially indicating an attempt to evade detection by breaking large files into smaller parts. Attackers may use this technique to bypass size-based security controls, facilitating the covert exfiltration of sensitive data. By monitoring for unusual or unauthorized use of the split syscall, this analytic helps identify potential data exfiltration attempts, allowing security teams to intervene and prevent the unauthorized transfer of critical information from the network.

1`linux_auditd` type=SYSCALL comm=split OR exe= "*/split" 
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_data_transfer_size_limits_via_split_syscall_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_data_transfer_size_limits_via_split_syscall_filter search *
linux_auditd_data_transfer_size_limits_via_split_syscall_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
T1030 Data Transfer Size Limits Exfiltration
KillChainPhase.ACTIONS_ON_OBJECTIVES
NistCategory.DE_AE
Cis18Value.CIS_10
APT28
APT41
LuminousMoth
Threat Group-3390

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

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

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
A SYSCALL - [$comm$] event was executed on host - [$dest$] that limits the size of data transfer. 25 50 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