| ID | Technique | Tactic |
|---|---|---|
| T1204.003 | Malicious Image | Execution |
| T1204 | User Execution | Execution |
Detection: ASL AWS ECR Container Upload Outside Business Hours
Description
The following analytic detects the upload of new containers to AWS Elastic Container Service (ECR) outside of standard business hours through AWS CloudTrail events. It identifies this behavior by monitoring for PutImage events occurring before 8 AM or after 8 PM, as well as any uploads on weekends. This activity is significant for a SOC to investigate as it may indicate unauthorized access or malicious deployments, potentially leading to compromised services or data breaches. Identifying and addressing such uploads promptly can mitigate the risk of security incidents and their associated impacts.
Search
1`amazon_security_lake` api.operation=PutImage
2| eval hour=strftime(time/pow(10,3), "%H"), weekday=strftime(time/pow(10,3), "%A")
3| where hour >= 20 OR hour < 8 OR weekday=Saturday OR weekday=Sunday
4| fillnull
5| stats count min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid actor.user.name actor.user.uid http_request.user_agent cloud.region
6| rename actor.user.name as user, cloud.region as region, http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id
7| `security_content_ctime(firstTime)`
8| `security_content_ctime(lastTime)`
9| `asl_aws_ecr_container_upload_outside_business_hours_filter`
Data Source
No data sources specified for this detection.
Macros Used
| Name | Value |
|---|---|
| amazon_security_lake | sourcetype=aws:cloudtrail:lake |
| asl_aws_ecr_container_upload_outside_business_hours_filter | search * |
asl_aws_ecr_container_upload_outside_business_hours_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
KillChainPhase.INSTALLATION
NistCategory.DE_AE
Cis18Value.CIS_13
TeamTNT
LAPSUS$
Scattered Spider
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
The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App.
Known False Positives
When your development is spreaded in different time zones, applying this rule can be difficult.
Associated Analytic Story
Risk Based Analytics (RBA)
| Risk Message | Risk Score | Impact | Confidence |
|---|---|---|---|
| Container uploaded outside business hours from $user$ | 49 | 70 | 70 |
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_asl |
aws:cloudtrail:lake |
| Integration | ✅ Passing | Dataset | aws_asl |
aws:cloudtrail:lake |
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