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.

Updated Date: 2024-10-17 ID: b4aefb5f-1037-410d-a149-1e091288ba33 Author: Abhinav Mishra, Kumar Sharad, Namratha Sreekanta and Xiao Lin, Splunk Type: Anomaly Product: Splunk Enterprise Security

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.

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

No data sources specified for this detection.

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

- MITRE ATT&CK
+ Kill Chain Phases
+ NIST
+ CIS
- Threat Actors
ID Technique Tactic
T1059 Command and Scripting Interpreter Execution
KillChainPhase.INSTALLATION
NistCategory.DE_AE
Cis18Value.CIS_10
APT19
APT32
APT37
APT39
Dragonfly
FIN5
FIN6
FIN7
Fox Kitten
Ke3chang
OilRig
Saint Bear
Stealth Falcon
Whitefly
Windigo
Winter Vivern

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

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

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: 3