| ID | Technique | Tactic |
|---|
Detection: Unusually Long Command Line - MLTK
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.
Description
The following analytic identifies unusually long command lines executed on hosts, which may indicate malicious activity. It leverages the Machine Learning Toolkit (MLTK) to detect command lines with lengths that deviate from the norm for a given user. This is significant for a SOC as unusually long command lines can be a sign of obfuscation or complex malicious scripts. If confirmed malicious, this activity could allow attackers to execute sophisticated commands, potentially leading to unauthorized access, data exfiltration, or further compromise of the system.
Search
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| search user!=unknown
8| apply cmdline_pdfmodel threshold=0.01
9| rename "IsOutlier(processlen)" as isOutlier
10| search isOutlier > 0
11| table firstTime lastTime user dest process_name process processlen count
12| `unusually_long_command_line___mltk_filter`
Data Source
| Name | Platform | Sourcetype | Source |
|---|---|---|---|
| CrowdStrike ProcessRollup2 | N/A | 'crowdstrike:events:sensor' |
'crowdstrike' |
| Sysmon EventID 1 |  Windows |
'xmlwineventlog' |
'XmlWinEventLog:Microsoft-Windows-Sysmon/Operational' |
| Windows Event Log Security 4688 | Windows |
'xmlwineventlog' |
'XmlWinEventLog:Security' |
Macros Used
| Name | Value |
|---|---|
| security_content_ctime | convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$) |
| unusually_long_command_line___mltk_filter | search * |
unusually_long_command_line___mltk_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
DE.AE
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. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "ESCU - Baseline of Command Line Length - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.
Known False Positives
Some legitimate applications use long command lines for installs or updates. You should review identified command lines for legitimacy. You may modify the first part of the search to omit legitimate command lines from consideration. If you are seeing more results than desired, you may consider changing the value of threshold in the search to a smaller value. You should also periodically re-run the support search to re-build the ML model on the latest data. You may get unexpected results if the user identified in the results is not present in the data used to build the associated model.
Associated Analytic Story
Risk Based Analytics (RBA)
Risk Message:
Unusually long command line usage on $dest$
| Risk Object | Risk Object Type | Risk Score | Threat Objects |
|---|---|---|---|
| user | user | 25 | No Threat Objects |
| dest | system | 25 | No Threat Objects |
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: 5