ID | Technique | Tactic |
---|---|---|
T1012 | Query Registry | Discovery |
T1049 | System Network Connections Discovery | Discovery |
T1069 | Permission Groups Discovery | Discovery |
T1016 | System Network Configuration Discovery | Discovery |
T1003 | OS Credential Dumping | Credential Access |
T1082 | System Information Discovery | Discovery |
T1115 | Clipboard Data | Collection |
T1552 | Unsecured Credentials | Credential Access |
Detection: Windows Post Exploitation Risk Behavior
Description
The following analytic identifies four or more distinct post-exploitation behaviors on a Windows system. It leverages data from the Risk data model in Splunk Enterprise Security, focusing on multiple risk events and their associated MITRE ATT&CK tactics and techniques. This activity is significant as it indicates potential malicious actions following an initial compromise, such as persistence, privilege escalation, or data exfiltration. If confirmed malicious, this behavior could allow attackers to maintain control, escalate privileges, and further exploit the compromised environment, leading to significant security breaches and data loss.
Search
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 from datamodel=Risk.All_Risk where All_Risk.analyticstories IN ("*Windows Post-Exploitation*") by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic
3| `drop_dm_object_name(All_Risk)`
4| `security_content_ctime(firstTime)`
5| `security_content_ctime(lastTime)`
6| where source_count >= 4
7| `windows_post_exploitation_risk_behavior_filter`
Data Source
Name | Platform | Sourcetype | Source | Supported App |
---|---|---|---|---|
N/A | N/A | N/A | N/A | N/A |
Macros Used
Name | Value |
---|---|
security_content_ctime | convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$) |
windows_post_exploitation_risk_behavior_filter | search * |
windows_post_exploitation_risk_behavior_filter
is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Annotations
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 |
Implementation
Splunk Enterprise Security is required to utilize this correlation. In addition, modify the source_count value to your environment. In our testing, a count of 4 or 5 was decent in a lab, but the number may need to be increased base on internal testing. In addition, based on false positives, modify any analytics to be anomaly and lower or increase risk based on organization importance.
Known False Positives
False positives will be present based on many factors. Tune the correlation as needed to reduce too many triggers.
Associated Analytic Story
Risk Based Analytics (RBA)
Risk Message | Risk Score | Impact | Confidence |
---|---|---|---|
An increase of Windows Post Exploitation behavior has been detected on $risk_object$ | 49 | 70 | 70 |
References
Detection Testing
Test Type | Status | Dataset | Source | Sourcetype |
---|---|---|---|---|
Validation | ✅ Passing | N/A | N/A | N/A |
Unit | ✅ Passing | Dataset | wpe |
stash |
Integration | ✅ Passing | Dataset | wpe |
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: 2