ID | Technique | Tactic |
---|---|---|
T1059.003 | Windows Command Shell | Execution |
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 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 | Supported App |
---|---|---|---|---|
CrowdStrike ProcessRollup2 | N/A | 'crowdstrike:events:sensor' |
'crowdstrike' |
N/A |
Macros Used
Name | Value |
---|---|
potentially_malicious_code_on_cmdline_tokenize_score | eval orig_process=process, process=replace(lower(process), " ", "") |
potentially_malicious_code_on_commandline_filter | search * |
potentially_malicious_code_on_commandline_filter
is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Annotations
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 |
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
Risk Based Analytics (RBA)
Risk Message | Risk Score | Impact | Confidence |
---|---|---|---|
Unusual command-line execution with command line length greater than 200 found on $dest$ with commandline value - [$process$] | 12 | 60 | 20 |
References
Detection Testing
Test Type | Status | Dataset | Source | Sourcetype |
---|---|---|---|---|
Validation | ✅ Passing | N/A | N/A | N/A |
Unit | ✅ Passing | Dataset | XmlWinEventLog:Microsoft-Windows-Sysmon/Operational |
xmlwineventlog |
Integration | ✅ Passing | Dataset | XmlWinEventLog:Microsoft-Windows-Sysmon/Operational |
xmlwineventlog |
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: 2