Excessive Usage Of SC Service Utility
Description
This search is to detect a suspicious excessive usage of sc.exe in a host machine. This technique was seen in several ransomware , xmrig and other malware to create, modify, delete or disable a service may related to security application or to gain privilege escalation.
- Type: Anomaly
-
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2021-06-24
- Author: Teoderick Contreras, Splunk
- ID: cb6b339e-d4c6-11eb-a026-acde48001122
Annotations
ATT&CK
Kill Chain Phase
- Installation
NIST
- DE.AE
CIS20
- CIS 10
CVE
Search
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`sysmon` EventCode = 1 process_name = "sc.exe"
| bucket _time span=15m
| stats values(process) as process count as numScExe by Computer, _time
| eventstats avg(numScExe) as avgScExe, stdev(numScExe) as stdScExe, count as numSlots by Computer
| eval upperThreshold=(avgScExe + stdScExe *3)
| eval isOutlier=if(avgScExe > 5 and avgScExe >= upperThreshold, 1, 0)
| search isOutlier=1
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `excessive_usage_of_sc_service_utility_filter`
Macros
The SPL above uses the following Macros:
excessive_usage_of_sc_service_utility_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
- EventCode
- process_name
- process
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. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed taskkill.exe may be used.
Known False Positives
excessive execution of sc.exe is quite suspicious since it can modify or execute app in high privilege permission.
Associated Analytic Story
RBA
Risk Score | Impact | Confidence | Message |
---|---|---|---|
25.0 | 50 | 50 | Excessive Usage Of SC Service Utility |
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
source | version: 1