Account Discovery With Net App
This search is to detect a potential account discovery series of command used by several malware or attack to recon the target machine. This technique is also seen in some note worthy malware like trickbot where it runs a cmd process, or even drop its module that will execute the said series of net command. This series of command are good correlation search and indicator of attacker recon if seen in the machines within a none technical user or department (HR, finance, ceo and etc) network.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2023-01-04
- Author: Teoderick Contreras, Splunk, TheLawsOfChaos, Github Community
- ID: 339805ce-ac30-11eb-b87d-acde48001122
Kill Chain Phase
1 2 3 4 5 6 7 | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_net` AND (Processes.process="* user *" OR Processes.process="*config*" OR Processes.process="*view /all*") by Processes.process_name Processes.dest Processes.user Processes.parent_process_name | where count >=4 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `account_discovery_with_net_app_filter`
The SPL above uses the following Macros:
account_discovery_with_net_app_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Supported Add-on (TA)
List of Splunk Add-on’s tested to work with the analytic.
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
Admin or power user may used this series of command.
Associated Analytic Story
|5.0||10||50||Suspicious $process_name$ usage detected on endpoint $dest$ by user $user$.|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
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: 4