Active Directory Lateral Movement Identified
The primary objective of this correlation rule is to detect and alert on potential lateral movement activities within an organization's Active Directory (AD) environment. By identifying multiple analytics associated with the Active Directory Lateral Movement analytic story, security analysts can gain better insight into possible threats and respond accordingly to mitigate risks. The correlation rule will trigger an alert when multiple analytics from the Active Directory Lateral Movement analytic story are detected within a specified time frame. The rule will generate an alert if a predetermined threshold of correlated analytics is reached within the specified time frame. This threshold can be customized to suit the needs and risk appetite of the organization.
- Type: Correlation
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Risk
- Last Updated: 2023-04-19
- Author: Michael Haag, Splunk
- ID: 6aa6f9dd-adfe-45a8-8f74-c4c7a0d7d037
Kill Chain Phase
- CIS 10
1 2 3 4 5 6 7 | 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="Active Directory Lateral Movement" All_Risk.risk_object_type="system" 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 | `active_directory_lateral_movement_identified_filter`
The SPL above uses the following Macros:
active_directory_lateral_movement_identified_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
List of fields required to use this analytic.
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 as the analytic story includes over 30 analytics. 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 most likely be present based on risk scoring and how the organization handles system to system communication. Filter, or modify as needed. In addition to count by analytics, adding a risk score may be useful. In our testing, with 22 events over 30 days, the risk scores ranged from 500 to 80,000. Your organization will be different, monitor and modify as needed.
Associated Analytic Story
|64.0||80||80||Activity related to lateral movement has been identified on $risk_object$.|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
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
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