ID | Technique | Tactic |
---|---|---|
T1059 | Command and Scripting Interpreter | Execution |
Detection: Detect Risky SPL using Pretrained ML Model
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 potentially risky SPL commands executed by users. It leverages a pretrained machine learning text classifier that analyzes command text, user, and search type to assign a risk score between 0 and 1. This detection is significant as it helps identify suspicious or unauthorized search activities that could indicate malicious intent or misuse of the Splunk environment. If confirmed malicious, such activity could lead to unauthorized data access, data exfiltration, or further exploitation of the system.
Search
1
2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Splunk_Audit.Search_Activity where Search_Activity.search_type=adhoc Search_Activity.user!=splunk-system-user by Search_Activity.search Search_Activity.user Search_Activity.search_type
3| eval spl_text = 'Search_Activity.search'. " " .'Search_Activity.user'. " " .'Search_Activity.search_type'
4| dedup spl_text
5| apply risky_spl_pre_trained_model
6| where risk_score > 0.5
7| `drop_dm_object_name(Search_Activity)`
8| table search, user, search_type, risk_score
9| `detect_risky_spl_using_pretrained_ml_model_filter`
Data Source
Name | Platform | Sourcetype | Source | Supported App |
---|---|---|---|---|
N/A | N/A | N/A | N/A | N/A |
Macros Used
Name | Value |
---|---|
security_content_summariesonly | summariesonly= summariesonly_config allow_old_summaries= oldsummaries_config fillnull_value= fillnull_config`` |
detect_risky_spl_using_pretrained_ml_model_filter | search * |
detect_risky_spl_using_pretrained_ml_model_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
This detection depends on the MLTK app which can be found here - https://splunkbase.splunk.com/app/2890/ and the Splunk Audit datamodel which can be found here - https://splunkbase.splunk.com/app/1621/. Additionally, you need to be ingesting logs which include Search_Activity.search, Search_Activity.user, Search_Activity.search_type from your endpoints. The risk score threshold should be adjusted based on the environment. The detection uses a custom MLTK model hence we need a few more steps for deployment, as outlined here - https://gist.github.com/ksharad-splunk/be2a62227966049047f5e5c4f2adcabb.
Known False Positives
False positives may be present if suspicious behavior is observed, as determined by frequent usage of risky keywords.
Associated Analytic Story
Risk Based Analytics (RBA)
Risk Message | Risk Score | Impact | Confidence |
---|---|---|---|
A potentially risky Splunk command has been run by $user$, kindly review. | 20 | 50 | 40 |
References
Detection Testing
Test Type | Status | Dataset | Source | Sourcetype |
---|---|---|---|---|
Validation | Not Applicable | N/A | N/A | N/A |
Unit | ✅ Passing | Dataset | audittrail |
audittrail |
Integration | ✅ Passing | Dataset | audittrail |
audittrail |
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