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Description

The following analytic detects excessive usage of net.exe or net1.exe within a one-minute interval. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line executions. This behavior is significant as it may indicate an adversary attempting to create, delete, or disable multiple user accounts rapidly, a tactic observed in Monero mining incidents. If confirmed malicious, this activity could lead to unauthorized user account manipulation, potentially compromising system integrity and enabling further malicious actions.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Endpoint
  • Last Updated: 2024-05-23
  • Author: Teoderick Contreras, Splunk
  • ID: 45e52536-ae42-11eb-b5c6-acde48001122

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1531 Account Access Removal Impact
Kill Chain Phase
  • Actions On Objectives
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


1
2
3
4
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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 `process_net` by Processes.process_name Processes.parent_process_name Processes.original_file_name Processes.dest Processes.user _time span=1m 
| where count >=10 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `excessive_usage_of_net_app_filter`

Macros

The SPL above uses the following Macros:

:information_source: excessive_usage_of_net_app_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.dest
  • Processes.user
  • Processes.parent_process_name
  • Processes.parent_process
  • Processes.original_file_name
  • Processes.process_name
  • Processes.process
  • Processes.process_id
  • Processes.parent_process_path
  • Processes.process_path
  • 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

unknown. Filter as needed. Modify the time span as needed.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
28.0 40 70 Excessive usage of net1.exe or net.exe within 1m, with command line $process$ has been detected on $dest$ by $user$

: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

source | version: 3