Detection: Potentially malicious code on commandline

Description

The following analytic detects potentially malicious command lines using a pretrained machine learning text classifier. It identifies unusual keyword combinations in command lines, such as "streamreader," "webclient," "mutex," "function," and "computehash," which are often associated with adversarial PowerShell code execution for C2 communication. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command lines longer than 200 characters. This activity is significant as it can indicate an attempt to execute malicious scripts, potentially leading to unauthorized code execution, data exfiltration, or further system compromise.

Search

 1
 2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.original_file_name) as original_file_name values(Processes.action) as action values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_path) as parent_process_path values(Processes.process_exec) as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) as process_integrity_level values(Processes.process_name) as process_name values(Processes.process_path) as process_path values(Processes.user) as user  values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product from datamodel="Endpoint.Processes" by Processes.parent_process_name Processes.process_name Processes.process Processes.user Processes.dest 
 3| `drop_dm_object_name(Processes)` 
 4| where len(process) > 200 
 5| `potentially_malicious_code_on_cmdline_tokenize_score` 
 6| apply unusual_commandline_detection 
 7| eval score='predicted(unusual_cmdline_logits)', process=orig_process 
 8| fields - unusual_cmdline* predicted(unusual_cmdline_logits) orig_process 
 9| where score > 0.5 
10| `security_content_ctime(firstTime)` 
11| `security_content_ctime(lastTime)` 
12| `potentially_malicious_code_on_commandline_filter`

Data Source

Name	Platform	Sourcetype	Source
Sysmon EventID 1	Windows	'XmlWinEventLog'	'XmlWinEventLog:Microsoft-Windows-Sysmon/Operational'
Windows Event Log Security 4688	Windows	'XmlWinEventLog'	'XmlWinEventLog:Security'
CrowdStrike ProcessRollup2	Other	'crowdstrike:events:sensor'	'crowdstrike'

Macros Used

Annotations

Default Configuration

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

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

This model is an anomaly detector that identifies usage of APIs and scripting constructs that are correllated with malicious activity. These APIs and scripting constructs are part of the programming langauge and advanced scripts may generate false positives.

Associated Analytic Story

• Suspicious Command-Line Executions

References

• https://attack.mitre.org/techniques/T1059/003/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md

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: 7