Linux At Application Execution
The following analytic identifies a suspicious process creation of At application. This process can be used by malware, adversaries and red teamers to create persistence entry to the targeted or compromised host with their malicious code. This anomaly detection can be a good indicator to investigate the event before and after this process execution, when it was executed and what schedule task it will execute.
- Type: Anomaly
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2022-05-26
- Author: Teoderick Contreras, Splunk
- ID: bf0a378e-5f3c-11ec-a6de-acde48001122
Kill Chain Phase
- CIS 3
- CIS 5
- CIS 16
1 2 3 4 5 6 | tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes where Processes.process_name IN ("at", "atd") OR Processes.parent_process_name IN ("at", "atd") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_at_application_execution_filter`
The SPL above uses the following Macros:
linux_at_application_execution_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
To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints into the Endpoint datamodel. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
Known False Positives
Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
Associated Analytic Story
|9.0||30||30||At application was executed in $dest$|
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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source | version: 2