This Splunk query identifies the use of Wake-on-LAN utilized by Ryuk ransomware. The Ryuk Ransomware uses the Wake-on-Lan feature to turn on powered off devices on a compromised network to have greater success encrypting them. This is a high fidelity indicator of Ryuk ransomware executing on an endpoint. Upon triage, isolate the endpoint. Additional file modification events will be within the users profile (\appdata\roaming) and in public directories (users\public). Review all Scheduled Tasks on the isolated endpoint and across the fleet. Suspicious Scheduled Tasks will include a path to a unknown binary and those endpoints should be isolated until triaged.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2021-03-01
- Author: Michael Haag, Splunk
- ID: 538d0152-7aaa-11eb-beaa-acde48001122
Kill Chain Phase
1 2 3 4 5 6 | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*8 LAN*" OR Processes.process="*9 REP*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ryuk_wake_on_lan_command_filter`
The SPL above uses the following Macros:
ryuk_wake_on_lan_command_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
Known False Positives
Limited to no known false positives.
Associated Analytic Story
|63.0||70||90||A process $process_name$ with wake on LAN commandline $process$ in host $dest$|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
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