Detection: Unusually Long Command Line

EXPERIMENTAL DETECTION

This detection status is set to experimental. The Splunk Threat Research team has not yet fully tested, simulated, or built comprehensive datasets for this detection. As such, this analytic is not officially supported. If you have any questions or concerns, please reach out to us at research@splunk.com.

Updated Date: 2024-05-20 ID: c77162d3-f93c-45cc-80c8-22f6a4264e7f Author: David Dorsey, Splunk Type: Anomaly Product: Splunk Enterprise Security

Description

The following analytic detects unusually long command lines, which may indicate malicious activity. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on the length of command lines executed on hosts. This behavior is significant because attackers often use obfuscated or complex command lines to evade detection and execute malicious payloads. If confirmed malicious, this activity could lead to data theft, ransomware deployment, or further system compromise. Analysts should investigate the source and content of the command line, inspect relevant artifacts, and review concurrent processes to identify potential threats.

 1
 2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process 
 3| `drop_dm_object_name("Processes")` 
 4| `security_content_ctime(firstTime)`
 5| `security_content_ctime(lastTime)`
 6|  eval processlen=len(process) 
 7| eventstats stdev(processlen) as stdev, avg(processlen) as avg by dest 
 8| stats max(processlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process 
 9| `unusually_long_command_line_filter` 
10|eval threshold = 3 
11| where maxlen > ((threshold*stdevperhost) + avgperhost)

Data Source

Name Platform Sourcetype Source Supported App
CrowdStrike ProcessRollup2 N/A 'crowdstrike:events:sensor' 'crowdstrike' N/A

Macros Used

Name Value
security_content_ctime convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)
unusually_long_command_line_filter search *
unusually_long_command_line_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
NistCategory.DE_AE
Cis18Value.CIS_10

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

Some legitimate applications start with long command lines.

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
Unusually long command line $process_name$ on $dest$ 42 70 60
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

Detection Testing

Test Type Status Dataset Source Sourcetype
Validation Not Applicable N/A N/A N/A
Unit ❌ Failing N/A N/A N/A
Integration ❌ Failing N/A N/A N/A

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: 6