This analytic identifies a suspicious processes running in file paths that are not typically associated with legitimate software. Adversaries often employ this technique to drop and execute malicious executables in accessible locations that do not require administrative privileges. By monitoring for processes running in such unconventional file paths, we can identify potential indicators of compromise and proactively respond to malicious activity. This analytic plays a crucial role in enhancing system security by pinpointing suspicious behaviors commonly associated with malware and unauthorized software execution.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2023-06-13
- Author: Teoderick Contreras, Splunk
- ID: 9be25988-ad82-11eb-a14f-acde48001122
Kill Chain Phase
- CIS 10
1 2 3 4 5 6 | tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_path = "*\\windows\\fonts\\*" OR Processes.process_path = "*\\windows\\temp\\*" OR Processes.process_path = "*\\users\\public\\*" OR Processes.process_path = "*\\windows\\debug\\*" OR Processes.process_path = "*\\Users\\Administrator\\Music\\*" OR Processes.process_path = "*\\Windows\\servicing\\*" OR Processes.process_path = "*\\Users\\Default\\*" OR Processes.process_path = "*Recycle.bin*" OR Processes.process_path = "*\\Windows\\Media\\*" OR Processes.process_path = "\\Windows\\repair\\*" OR Processes.process_path = "*\\temp\\*" OR Processes.process_path = "*\\PerfLogs\\*" by Processes.parent_process_name Processes.parent_process Processes.process_path Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_process_file_path_filter`
The SPL above uses the following Macros:
suspicious_process_file_path_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
The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the
Processes node of the
Endpoint data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
Known False Positives
Administrators may allow execution of specific binaries in non-standard paths. Filter as needed.
Associated Analytic Story
- Data Destruction
- Hermetic Wiper
- DarkCrystal RAT
- Graceful Wipe Out Attack
- Swift Slicer
- RedLine Stealer
- Brute Ratel C4
- Prestige Ransomware
- LockBit Ransomware
- Double Zero Destructor
- Volt Typhoon
- Chaos Ransomware
- BlackByte Ransomware
- Warzone RAT
- DarkGate Malware
|35.0||70||50||Suspicious process $process_name$ running from a suspicious process path- $process_path$ on host- $dest$|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
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