Detection: Linux Obfuscated Files or Information Base64 Decode

Description

The following analytic detects the use of the base64 decode command on Linux systems, which is often used to deobfuscate files. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that include "base64 -d" or "base64 --decode". This activity is significant as it may indicate an attempt to hide malicious payloads or scripts. If confirmed malicious, an attacker could use this technique to execute hidden code, potentially leading to unauthorized access, data exfiltration, or further system compromise.

1
2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_path="*/base64" Processes.process="*-d*" by Processes.process Processes.dest Processes.process_current_directory Processes.process_name Processes.process_integrity_level Processes.parent_process_name Processes.parent_process_path Processes.parent_process_guid Processes.parent_process_id Processes.process_guid Processes.process_id Processes.user 
3| `drop_dm_object_name(Processes)` 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)` 
6| `linux_obfuscated_files_or_information_base64_decode_filter`

Data Source

Name Platform Sourcetype Source Supported App
Sysmon for Linux EventID 1 Linux icon Linux 'sysmon:linux' 'Syslog:Linux-Sysmon/Operational' N/A

Macros Used

Name Value
security_content_ctime convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)
linux_obfuscated_files_or_information_base64_decode_filter search *
linux_obfuscated_files_or_information_base64_decode_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
T1027 Obfuscated Files or Information Defense Evasion
KillChainPhase.EXPLOITAITON
NistCategory.DE_AE
Cis18Value.CIS_10
APT-C-36
APT3
APT37
APT41
BackdoorDiplomacy
BlackOasis
Earth Lusca
Ember Bear
GALLIUM
Gallmaker
Gamaredon Group
Ke3chang
Kimsuky
Mustang Panda
Rocke
Sandworm Team
Windshift

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

The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the Processes node of the Endpoint data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.

Known False Positives

False positives may be present and will require some tuning based on processes. Filter as needed.

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ decoding base64. 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 Syslog:Linux-Sysmon/Operational sysmon:linux
Integration ✅ Passing Dataset Syslog:Linux-Sysmon/Operational sysmon:linux

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: 4