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Description

The following analytic identifies unusual modifications to security groups in your cloud environment by users, focusing on actions such as modifications, deletions, or creations over 30-minute intervals. It leverages cloud infrastructure logs and calculates the standard deviation for each user, using the 3-sigma rule to detect anomalies. This activity is significant as it may indicate a compromised account or insider threat. If confirmed malicious, attackers could alter security group configurations, potentially exposing sensitive resources or disrupting services.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Change
  • Last Updated: 2024-05-18
  • Author: Bhavin Patel, Splunk
  • ID: cfe7cca7-2746-4bdf-b712-b01ed819b9de

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1578.005 Modify Cloud Compute Configurations Defense Evasion
Kill Chain Phase
  • Exploitation
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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| tstats dc(All_Changes.object) as unique_security_groups values(All_Changes.src) as src values(All_Changes.user_type) as user_type values(All_Changes.object_category) as object_category values(All_Changes.object) as objects values(All_Changes.action) as action  values(All_Changes.user_agent) as user_agent values(All_Changes.command) as command from datamodel=Change WHERE All_Changes.object_category = "security_group" (All_Changes.action = modified OR All_Changes.action = deleted OR All_Changes.action = created)  by All_Changes.user  _time span=30m 
|  `drop_dm_object_name("All_Changes")` 
| eventstats avg(unique_security_groups) as avg_changes , stdev(unique_security_groups) as std_changes by user 
| eval upperBound=(avg_changes+std_changes*3) 
| eval isOutlier=if(unique_security_groups > 2 and unique_security_groups >= upperBound, 1, 0) 
| where isOutlier=1
| `cloud_security_groups_modifications_by_user_filter`

Macros

The SPL above uses the following Macros:

:information_source: cloud_security_groups_modifications_by_user_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
  • All_Changes.object_id
  • All_Changes.action
  • All_Changes.status
  • All_Changes.object_category
  • All_Changes.user

How To Implement

This search requries the Cloud infrastructure logs such as AWS Cloudtrail, GCP Pubsub Message logs, Azure Audit logs to be ingested into an accelerated Change datamodel. It is also recommended that users can try different combinations of the bucket span time and outlier conditions to better suit with their environment.

Known False Positives

It is possible that legitimate user/admin may modify a number of security groups

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
35.0 70 50 Unsual number cloud security group modifications detected by 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: 2