Detection: Windows AppLocker Execution from Uncommon Locations

Updated Date: 2024-10-17 ID: d57ce957-151a-4aec-ada5-5fb1eb555b6b Author: Michael Haag, Splunk Type: Hunting Product: Splunk Enterprise Security

Description

The following analytic identifies the execution of applications or scripts from uncommon or suspicious file paths, potentially indicating malware or unauthorized activity. It leverages Windows AppLocker event logs and uses statistical analysis to detect anomalies. By calculating the average and standard deviation of execution counts per file path, it flags paths with execution counts significantly higher than expected. This behavior is significant as it can uncover malicious activities or policy violations. If confirmed malicious, this activity could allow attackers to execute unauthorized code, leading to potential system compromise or data breaches.

Search

 1`applocker` 
 2| spath input=UserData_Xml 
 3| rename RuleAndFileData.* as *, Computer as dest, TargetUser AS user 
 4| stats count min(_time) as firstTime max(_time) as lastTime by dest, PolicyName, RuleId, user, TargetProcessId, FilePath, FullFilePath 
 5| eventstats avg(count) as avg, stdev(count) as stdev 
 6| eval upperBound=(avg+stdev*2), anomaly=if(count > upperBound, "Yes", "No") 
 7| where anomaly="Yes" 
 8| `security_content_ctime(firstTime)` 
 9| `security_content_ctime(lastTime)` 
10| `windows_applocker_execution_from_uncommon_locations_filter`

Data Source

No data sources specified for this detection.

Macros Used

Name	Value
applocker	`(source="WinEventLog:Microsoft-Windows-AppLocker/" OR source="XmlWinEventLog:Microsoft-Windows-AppLocker/")`
windows_applocker_execution_from_uncommon_locations_filter	`search *`

windows_applocker_execution_from_uncommon_locations_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
T1218	System Binary Proxy Execution	Defense Evasion

KillChainPhase.EXPLOITAITON

NistCategory.DE_AE

Cis18Value.CIS_10

Lazarus Group

Volt Typhoon

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	False

This configuration file applies to all detections of type hunting.

Implementation

The analytic is designed to be run against Windows AppLocker event logs collected from endpoints with AppLocker enabled. If using Microsoft Defender for Endpoint (MDE), modify the analytic to use EventTypes/ActionTypes that match the block events for AppLocker. The analytic requires the AppLocker event logs to be ingested into Splunk. Note that, an additional method to reduce any false positives would be to add the specific EventCodes - 8003 or 8004 and filter from there. Upon tuning, modify to Anomaly or TTP.

Known False Positives

False positives are possible if legitimate users are executing applications from file paths that are not permitted by AppLocker. It is recommended to investigate the context of the application execution to determine if it is malicious or not. Modify the threshold as needed to reduce false positives.

Associated Analytic Story

Windows AppLocker

Risk Based Analytics (RBA)

Risk Message	Risk Score	Impact	Confidence
An application was executed from an uncommon location on a host $dest$.	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	`XmlWinEventLog:Microsoft-Windows-AppLocker/MSI and Script`	`XmlWinEventLog`
Integration	✅ Passing	Dataset	`XmlWinEventLog:Microsoft-Windows-AppLocker/MSI and Script`	`XmlWinEventLog`

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: 3