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Description

The following analytic, "Suspicious Scheduled Task from Public Directory", detects the registration of scheduled tasks aimed to execute a binary or script from public directories, a behavior often associated with malware deployment. It utilizes the Sysmon Event ID 1 data source, searching for instances where schtasks.exe is connected with the directories users\public, \programdata\, or \windows\temp and involves the /create command.
The registration of such scheduled tasks in public directories could suggest that an attacker is trying to maintain persistence or execute malicious scripts. If confirmed as a true positive, this could lead to data compromise, unauthorized access, and potential lateral movement within the network.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Endpoint
  • Last Updated: 2021-03-01
  • Author: Michael Haag, Splunk
  • ID: 7feb7972-7ac3-11eb-bac8-acde48001122

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1053.005 Scheduled Task Execution, Persistence, Privilege Escalation
T1053 Scheduled Task/Job Execution, Persistence, Privilege Escalation
Kill Chain Phase
  • Installation
  • Exploitation
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


1
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| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*\\users\\public\\* OR Processes.process=*\\programdata\\* OR Processes.process=*windows\\temp*)  Processes.process=*/create* by Processes.dest Processes.user 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)`
| `suspicious_scheduled_task_from_public_directory_filter`

Macros

The SPL above uses the following Macros:

:information_source: suspicious_scheduled_task_from_public_directory_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.

  • _time
  • Processes.process_name
  • Processes.process
  • Processes.dest
  • Processes.user
  • Processes.parent_process
  • Processes.process_name
  • Processes.process_id
  • Processes.parent_process_id

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

The main source of false positives could be the legitimate use of scheduled tasks from these directories. Careful tuning of this search may be necessary to suit the specifics of your environment, reducing the rate of false positives.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
35.0 70 50 Suspicious scheduled task registered on $dest$ from Public Directory

: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.

Reference

Test Dataset

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

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