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Description

The following analytic detects suspicious Base64 decode operations that may indicate malicious activity, such as data exfiltration or execution of encoded commands. Base64 is commonly used to encode data for safe transmission, but attackers may abuse it to conceal malicious payloads. This detection focuses on identifying unusual or unexpected Base64 decoding processes, particularly when associated with critical files or directories. By monitoring these activities, the analytic helps uncover potential threats, enabling security teams to respond promptly and mitigate risks associated with encoded malware or unauthorized data access.

  • Type: Anomaly

  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

  • Last Updated: 2024-09-04
  • Author: Teoderick Contreras, Splunk
  • ID: 5890ba10-4e48-4dc0-8a40-3e1ebe75e737

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1140 Deobfuscate/Decode Files or Information Defense Evasion
Kill Chain Phase
  • Exploitation
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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`linux_auditd` `linux_auditd_normalized_execve_process` 
| rename host as dest 
| where LIKE(process_exec, "%base64%") AND (LIKE(process_exec, "%-d %") OR LIKE(process_exec, "% --d%")) 
| stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `linux_auditd_base64_decode_files_filter`

Macros

The SPL above uses the following Macros:

:information_source: linux_auditd_base64_decode_files_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
  • argc
  • process_exec

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

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

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
25.0 50 50 A [$process_exec$] event occurred on host - [$dest$] to decode a file using base64.

:information_source: 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