Detect suspicious processnames using pretrained model in DSDL

SPLUNK detection rule by Abhinav Mishra, Kumar Sharad and Namratha Sreekanta, Splunk · Apache-2.0

The following analytic identifies suspicious process names using a pre-trained Deep Learning model. It leverages Endpoint Detection and Response (EDR) telemetry to analyze process names and predict their likelihood of being malicious. The model, a character-level Recurrent Neural Network (RNN), classifies process names as benign or suspicious based on a threshold score of 0.5. This detection is significant as it helps identify malware, such as TrickBot, which often uses randomly generated filenames to evade detection. If confirmed malicious, this activity could indicate the presence of malware capable of propagating across the network and executing harmful actions.

name: Detect suspicious processnames using pretrained model in DSDL
id: a15f8977-ad7d-4669-92ef-b59b97219bf5
version: 9
creation_date: '2023-04-04'
modification_date: '2026-05-13'
author: Abhinav Mishra, Kumar Sharad and Namratha Sreekanta, Splunk
type: Anomaly
status: removed
data_source:
    - Sysmon EventID 1
description: The following analytic identifies suspicious process names using a pre-trained Deep Learning model. It leverages Endpoint Detection and Response (EDR) telemetry to analyze process names and predict their likelihood of being malicious. The model, a character-level Recurrent Neural Network (RNN), classifies process names as benign or suspicious based on a threshold score of 0.5. This detection is significant as it helps identify malware, such as TrickBot, which often uses randomly generated filenames to evade detection. If confirmed malicious, this activity could indicate the presence of malware capable of propagating across the network and executing harmful actions.
search: |-
    | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes
      BY Processes.action Processes.dest Processes.original_file_name
         Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid
         Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path
         Processes.process Processes.process_exec Processes.process_guid
         Processes.process_hash Processes.process_id Processes.process_integrity_level
         Processes.process_name Processes.process_path Processes.user
         Processes.user_id Processes.vendor_product
    | `drop_dm_object_name(Processes)`
    | rename process_name as text
    | fields text, parent_process_name, process, user, dest
    | apply detect_suspicious_processnames_using_pretrained_model_in_dsdl
    | rename predicted_label as is_suspicious_score
    | rename text as process_name
    | where is_suspicious_score > 0.5
    | `detect_suspicious_processnames_using_pretrained_model_in_dsdl_filter`
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: False positives may be present if a suspicious processname is similar to a benign processname.
references:
    - https://www.cisa.gov/uscert/ncas/alerts/aa20-302a
    - https://www.splunk.com/en_us/blog/security/random-words-on-entropy-and-dns.html
rba:
    message: The process $process$ is running from an unusual place by $user$ on $dest$ with a processname that appears to be randomly generated.
    risk_objects:
        - field: dest
          type: system
          score: 20
        - field: user
          type: user
          score: 20
    threat_objects: []
tags:
    analytic_story:
        - Suspicious Command-Line Executions
    asset_type: Endpoint
    mitre_attack_id:
        - T1059
    product:
        - Splunk Enterprise
        - Splunk Enterprise Security
        - Splunk Cloud
    security_domain: endpoint
deprecation_info:
    reason: Detection is deprecated as these do not work with the latest Splunk AI Toolkit(5.7.0) and Python for Scientific Computing for Linux 64-bit(4.3.0).
    removed_in_version: 5.26.0
    replacement_content: []

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