Detection: Excessive Usage Of Net App

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

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.

1
2| 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 
3| where count >=10 
4| `drop_dm_object_name(Processes)` 
5| `security_content_ctime(firstTime)` 
6| `security_content_ctime(lastTime)` 
7| `excessive_usage_of_net_app_filter`

Data Source

Name Platform Sourcetype Source Supported App
CrowdStrike ProcessRollup2 N/A 'crowdstrike:events:sensor' 'crowdstrike' N/A

Macros Used

Name Value
process_net (Processes.process_name="net.exe" OR Processes.original_file_name="net.exe" OR Processes.process_name="net1.exe" OR Processes.original_file_name="net1.exe")
excessive_usage_of_net_app_filter search *
excessive_usage_of_net_app_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Annotations

- MITRE ATT&CK
+ Kill Chain Phases
+ NIST
+ CIS
- Threat Actors
ID Technique Tactic
T1531 Account Access Removal Impact
KillChainPhase.ACTIONS_ON_OBJECTIVES
NistCategory.DE_AE
Cis18Value.CIS_10
Akira
LAPSUS$

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Setting Value
Disabled true
Cron Schedule 0 * * * *
Earliest Time -70m@m
Latest Time -10m@m
Schedule Window auto
Creates Risk Event True
This configuration file applies to all detections of type anomaly. These detections will use Risk Based Alerting.

Implementation

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

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
Excessive usage of net1.exe or net.exe within 1m, with command line $process$ has been detected on $dest$ by $user$ 28 40 70
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

References

Detection Testing

Test Type Status Dataset Source Sourcetype
Validation ✅ Passing N/A N/A N/A
Unit ✅ Passing Dataset XmlWinEventLog:Microsoft-Windows-Sysmon/Operational xmlwineventlog
Integration ✅ Passing Dataset XmlWinEventLog:Microsoft-Windows-Sysmon/Operational xmlwineventlog

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: GitHub | Version: 3