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This analytic is designed to identify executions of applications or scripts from uncommon or suspicious file paths, which could be indicative of malware or unauthorized activity. By leveraging a simple statistical model, the query analyzes the frequency of application executions across different file paths. It calculates the average (avg) number of executions per file path and uses the standard deviation (stdev) to measure the variation or dispersion of the execution counts from the average. A file path is considered uncommon or suspicious if the number of executions from it is significantly higher than what is expected based on the calculated average and standard deviation. Specifically, the analytic flags any file path from which the number of executions exceeds the upper bound, defined as the average plus two times the standard deviation (avg+stdev*2). This approach helps in pinpointing anomalies in application execution patterns, potentially uncovering malicious activities or policy violations.

  • Type: Hunting
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

  • Last Updated: 2024-03-21
  • Author: Michael Haag, Splunk
  • ID: d57ce957-151a-4aec-ada5-5fb1eb555b6b




ID Technique Tactic
T1218 System Binary Proxy Execution Defense Evasion
Kill Chain Phase
  • Exploitation
  • DE.AE
  • CIS 10
| spath input=UserData_Xml 
| rename RuleAndFileData.* as *, Computer as dest, TargetUser AS user 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, PolicyName, RuleId, user, TargetProcessId, FilePath, FullFilePath 
| eventstats avg(count) as avg, stdev(count) as stdev 
| eval upperBound=(avg+stdev*2), anomaly=if(count > upperBound, "Yes", "No") 
| where anomaly="Yes" 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `windows_applocker_execution_from_uncommon_locations_filter`


The SPL above uses the following Macros:

:information_source: windows_applocker_execution_from_uncommon_locations_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.

  • FullFilePath
  • dest
  • user

How To Implement

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


Risk Score Impact Confidence Message
49.0 70 70 An application was executed from an uncommon location on a host $dest$.

: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.


Test Dataset

Replay any dataset to Splunk Enterprise by using our tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

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