This analytic identifies excessive usage of
net1.exe within a bucket of time (1 minute). This behavior was seen in a Monero incident where the adversary attempts to create many users, delete and disable users as part of its malicious behavior.
- Type: Anomaly
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2021-05-06
- Author: Teoderick Contreras, Splunk
- ID: 45e52536-ae42-11eb-b5c6-acde48001122
Kill Chain Phase
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 `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`
The SPL above uses the following Macros:
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.
List of fields required to use this analytic.
How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the
Endpoint datamodel in the
Processes node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
Known False Positives
unknown. Filter as needed. Modify the time span as needed.
Associated Analytic Story
|28.0||40||70||Excessive usage of net1.exe or net.exe within 1m, with command line $process$ has been detected on $dest$ by $user$|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
source | version: 2