This analytic will identify suspicious series of command-line to disable several services. This technique is seen where the adversary attempts to disable security app services or other malware services to complete the objective on the compromised system.
- Type: Anomaly
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2021-05-04
- Author: Teoderick Contreras, Splunk
- ID: 8fa2a0f0-acd9-11eb-8994-acde48001122
Kill Chain Phase
- Actions On Objectives
- CIS 10
1 2 3 4 5 6 7 | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "sc.exe" AND Processes.process="*config*" OR Processes.process="*Disabled*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user _time span=1m | where count >=4 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_attempt_to_disable_services_filter`
The SPL above uses the following Macros:
excessive_attempt_to_disable_services_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
Associated Analytic Story
|80.0||80||100||An excessive amount of $process_name$ was executed on $dest$ attempting to disable services.|
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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