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Description

This search correlations detections by user and risk_score

  • Type: Correlation
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

  • Last Updated: 2021-09-06
  • Author: Patrick Bareiss, Splunk
  • ID: 610e12dc-b6fa-4541-825e-4a0b3b6f6773

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1204.003 Malicious Image Execution
T1204 User Execution Execution
Kill Chain Phase
  • Actions on Objectives
NIST
  • PR.DS
  • PR.AC
  • DE.CM
CIS20
  • CIS 13
CVE
1
2
3
4
5
6
`signals` 
| fillnull 
| stats sum(risk_score) as risk_score values(source) as signals values(repository) as repository by user 
| sort - risk_score 
| where risk_score > 80 
| `correlation_by_user_and_risk_filter`

Macros

The SPL above uses the following Macros:

:information_source: correlation_by_user_and_risk_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

How To Implement

For Dev Sec Ops POC

Known False Positives

unknown

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
70.0 70 100 Correlation triggered for user $user$

: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: 1