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Description

This correlation search looks at the risk events created by the detection analytics related Collection and Exfiltration techniques used by adversaries. The rule is designed to identify instances where 2 or more analytics unique AWS analytics and 2 or more distinct mitre IDs has triggered for a particular risk object. This alert when triggered may indicate a potential exfiltration in progress. By aggregating these analytics, security teams can swiftly respond to and investigate any suspicious activities, enhancing their ability to protect critical assets and prevent unauthorized access to sensitive information.

  • Type: Correlation
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Risk
  • Last Updated: 2023-05-04
  • Author: Bhavin Patel, Splunk
  • ID: 85096389-a443-42df-b89d-200efbb1b560

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1537 Transfer Data to Cloud Account Exfiltration
Kill Chain Phase
  • Actions On Objectives
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count values(All_Risk.risk_message) as risk_message  from datamodel=Risk.All_Risk where All_Risk.annotations.mitre_attack.mitre_tactic = "collection" OR All_Risk.annotations.mitre_attack.mitre_tactic = "exfiltration" source = *AWS*  by All_Risk.risk_object 
| `drop_dm_object_name(All_Risk)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| where source_count >= 2 and mitre_tactic_id_count>=2 
| `aws_s3_exfiltration_behavior_identified_filter`

Macros

The SPL above uses the following Macros:

:information_source: aws_s3_exfiltration_behavior_identified_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.

  • All_Risk.analyticstories
  • All_Risk.risk_object_type
  • All_Risk.risk_object
  • All_Risk.annotations.mitre_attack.mitre_tactic
  • All_Risk.calculated_risk_score
  • source

How To Implement

You must enable all the detection searches in the Data Exfiltration Analytic story to create risk events in Enterprise Security.

Known False Positives

alse positives may be present based on automated tooling or system administrators. Filter as needed.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
81.0 90 90 Multiple AWS Exfiltration detections $source$ and techniques $All_Risk.annotations.mitre_attack.mitre_tactic_id$ trigged for risk object $risk_object$

: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