THIS IS A EXPERIMENTAL DETECTION
This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.
The following analytic is designed to detect anomalous behavior associated with the BlackLotus Campaign, a sophisticated bootkit attack reported by ESET and further investigated in a blog by Microsoft, which provided hunting queries for security analysts. The primary focus of this analytic is to identify instances of Winlogon.exe, a critical Windows process, connecting to public IP space, which is indicative of potential malicious activity.\ The BlackLotus Campaign is a bootkit-based attack that compromises system integrity by infecting the Master Boot Record (MBR) and Volume Boot Record (VBR). This malware variant can bypass traditional security measures, load before the operating system, and maintain persistence on the target system.
Winlogon.exe is a critical Windows process responsible for managing user logon and logoff processes. Under normal circumstances, Winlogon.exe should not be connecting to public IP addresses. However, if it does, it may indicate that the process has been compromised as part of the BlackLotus Campaign or another malicious operation.
This analytic monitors network connections made by Winlogon.exe and triggers an alert if it detects connections to public IP space. By identifying such anomalous behavior, security analysts can investigate further and respond swiftly to potential threats.
- Type: Hunting
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint, Network_Traffic
- Last Updated: 2023-05-03
- Author: Michael Haag, Splunk
- ID: 65615b3a-62ea-4d65-bb9f-6f07c17df4ea
Kill Chain Phase
- CIS 10
1 2 3 4 5 6 7 8 9 10 11 | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN (winlogon.exe) Processes.process!=unknown by Processes.dest Processes.user Processes.parent_process_name 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)` | join process_id [ | tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port != 0 NOT (All_Traffic.dest IN (127.0.0.1,10.0.0.0/12,172.16.0.0/12, 192.168.0.0/16, 0:0:0:0:0:0:0:1)) by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name(All_Traffic)` | rename dest as publicIp ] | table dest parent_process_name process_name process_path process process_id dest_port publicIp | `windows_winlogon_with_public_network_connection_filter`
The SPL above uses the following Macros:
windows_winlogon_with_public_network_connection_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
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
False positives will be present and filtering will be required. Legitimate IPs will be present and need to be filtered.
Associated Analytic Story
|25.0||50||50||Winlogon.exe has generated a network connection to a remote destination on endpoint $dest$.|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
source | version: 1