Detection: AWS S3 Exfiltration Behavior Identified

Description

The following analytic identifies potential AWS S3 exfiltration behavior by correlating multiple risk events related to Collection and Exfiltration techniques. It leverages risk events from AWS sources, focusing on instances where two or more unique analytics and distinct MITRE ATT&CK IDs are triggered for a specific risk object. This activity is significant as it may indicate an ongoing data exfiltration attempt, which is critical for security teams to monitor. If confirmed malicious, this could lead to unauthorized access and theft of sensitive information, compromising the organization's data integrity and confidentiality.

1
2| 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 
3| `drop_dm_object_name(All_Risk)` 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)` 
6| where source_count >= 2 and mitre_tactic_id_count>=2 
7| `aws_s3_exfiltration_behavior_identified_filter`

Data Source

No data sources specified for this detection.

Macros Used

Name Value
security_content_ctime convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)
aws_s3_exfiltration_behavior_identified_filter search *
aws_s3_exfiltration_behavior_identified_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
T1537 Transfer Data to Cloud Account Exfiltration
KillChainPhase.ACTIONS_ON_OBJECTIVES
NistCategory.DE_AE
Cis18Value.CIS_10
INC Ransom
RedCurl

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 Notable Yes
Rule Title %name%
Rule Description %description%
Notable Event Fields user, dest
Creates Risk Event False
This configuration file applies to all detections of type Correlation. These correlations will generate Notable Events.

Implementation

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

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
Multiple AWS Exfiltration detections $source$ and techniques $annotations.mitre_attack.mitre_tactic_id$ trigged for risk object $risk_object$ 81 90 90
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 Passing N/A N/A N/A
Unit Passing Dataset aws_exfil stash
Integration ✅ Passing Dataset aws_exfil stash

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: 4