Detection: Living Off The Land Detection

Updated Date: 2024-11-13 ID: 1be30d80-3a39-4df9-9102-64a467b24abc Author: Michael Haag, Splunk Type: Correlation Product: Splunk Enterprise Security

Description

The following correlation identifies multiple risk events associated with the "Living Off The Land" analytic story, indicating potentially suspicious behavior. It leverages the Risk data model to aggregate and correlate events tagged under this story, focusing on systems with a high count of distinct sources. This activity is significant as it often involves the use of legitimate tools for malicious purposes, making detection challenging. If confirmed malicious, this behavior could allow attackers to execute code, escalate privileges, or persist within the environment using trusted system utilities.

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="Living Off The Land" All_Risk.risk_object_type="system" 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 >= 5 
7| `living_off_the_land_detection_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($field$)
living_off_the_land_detection_filter search *
living_off_the_land_detection_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
T1105 Ingress Tool Transfer Command And Control
T1190 Exploit Public-Facing Application Initial Access
T1059 Command and Scripting Interpreter Execution
T1133 External Remote Services Initial Access
Command and Control
Delivery
Installation
DE.AE
CIS 10

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

To implement this correlation search a user needs to enable all detections in the Living Off The Land Analytic Story and confirm it is generating risk events. A simple search index=risk analyticstories="Living Off The Land" should contain events.

Known False Positives

There are no known false positive for this search, but it could contain false positives as multiple detections can trigger and not have successful exploitation. Modify the static value distinct_detection_name to a higher value. It is also required to tune analytics that are also tagged to ensure volume is never too much.

Associated Analytic Story

References

Detection Testing

Test Type Status Dataset Source Sourcetype
Validation ✅ Passing N/A N/A N/A
Unit ✅ Passing Dataset lotl stash
Integration ✅ Passing Dataset lotl 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: 5