This analytic look for a spawned process of route.exe windows application. Adversaries and red teams alike abuse this application the recon or do a network discovery on a target host. but one possible false positive might be an automated tool used by a system administator or a powershell script in amazon ec2 config services.
- Type: Hunting
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2021-11-12
- Author: Teoderick Contreras, Splunk
- ID: dd83407e-439f-11ec-ab8e-acde48001122
Kill Chain Phase
- CIS 10
1 2 3 4 5 6 | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_route` by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `network_discovery_using_route_windows_app_filter`
The SPL above uses the following Macros:
network_discovery_using_route_windows_app_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
A network operator or systems administrator may utilize an automated host discovery application that may generate false positives or an amazon ec2 script that uses this application. Filter as needed.
Associated Analytic Story
|9.0||30||30||Network Connection discovery on $dest$ by $user$|
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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