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Description

The following correlation identifies a four or more number of distinct analytics associated with the Windows Post-Exploitation analytic story, which enables the identification of potentially suspicious behavior. Windows Post-Exploitation refers to the phase that occurs after an attacker successfully compromises a Windows system. During this stage, attackers strive to maintain persistence, gather sensitive information, escalate privileges, and exploit the compromised environment further. Timely detection of post-exploitation activities is crucial for prompt response and effective mitigation. Common post-exploitation detections encompass identifying suspicious processes or services running on the system, detecting unusual network connections or traffic patterns, identifying modifications to system files or registry entries, monitoring abnormal user account activities, and flagging unauthorized privilege escalations. Ensuring the detection of post-exploitation activities is essential to proactively prevent further compromise, minimize damage, and restore the security of the Windows environment.

  • Type: Correlation
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Risk
  • Last Updated: 2023-06-14
  • Author: Teoderick Contreras, Splunk
  • ID: edb930df-64c2-4bb7-9b5c-889ed53fb973

Annotations

ATT&CK

ATT&CK

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
Kill Chain Phase
  • Exploitation
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 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 
| `drop_dm_object_name(All_Risk)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| where source_count >= 4 
| `windows_post_exploitation_risk_behavior_filter`

Macros

The SPL above uses the following Macros:

:information_source: windows_post_exploitation_risk_behavior_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_Risk.analyticstories
  • All_Risk.risk_object_type
  • All_Risk.risk_object
  • All_Risk.annotations.mitre_attack.mitre_tactic
  • source

How To Implement

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

RBA

Risk Score Impact Confidence Message
49.0 70 70 An increase of Windows Post Exploitation behavior has been detected on $risk_object$

: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