:warning: THIS IS A EXPERIMENTAL DETECTION

This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.

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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.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Splunk_Audit
  • Last Updated: 2024-05-26
  • Author: Abhinav Mishra, Kumar Sharad, Namratha Sreekanta and Xiao Lin, Splunk
  • ID: b4aefb5f-1037-410d-a149-1e091288ba33

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1059 Command and Scripting Interpreter Execution
Kill Chain Phase
  • Installation
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


1
2
3
4
5
6
7
8
9



| 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 
| eval spl_text = 'Search_Activity.search'. " " .'Search_Activity.user'. " " .'Search_Activity.search_type'
| dedup spl_text 
| apply risky_spl_pre_trained_model 
| where risk_score > 0.5 
| `drop_dm_object_name(Search_Activity)` 
| table search, user, search_type, risk_score 
| `detect_risky_spl_using_pretrained_ml_model_filter`

Macros

The SPL above uses the following Macros:

:information_source: detect_risky_spl_using_pretrained_ml_model_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Required fields

List of fields required to use this analytic.

  • _time
  • Search_Activity.search
  • Search_Activity.user
  • Search_Activity.search_type

How To Implement

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

RBA

Risk Score Impact Confidence Message
20.0 50 40 A potentially risky Splunk command has been run by $user$, kindly review.

:information_source: The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

Reference

Test Dataset

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 | version: 2