Detection: Detect suspicious processnames using pretrained model in DSDL

Description

The following analytic identifies suspicious process names using a pre-trained Deep Learning model. It leverages Endpoint Detection and Response (EDR) telemetry to analyze process names and predict their likelihood of being malicious. The model, a character-level Recurrent Neural Network (RNN), classifies process names as benign or suspicious based on a threshold score of 0.5. This detection is significant as it helps identify malware, such as TrickBot, which often uses randomly generated filenames to evade detection. If confirmed malicious, this activity could indicate the presence of malware capable of propagating across the network and executing harmful actions.

Search

 1
 2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes
 3  BY Processes.action Processes.dest Processes.original_file_name
 4     Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid
 5     Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path
 6     Processes.process Processes.process_exec Processes.process_guid
 7     Processes.process_hash Processes.process_id Processes.process_integrity_level
 8     Processes.process_name Processes.process_path Processes.user
 9     Processes.user_id Processes.vendor_product
10
11| `drop_dm_object_name(Processes)`
12
13| rename process_name as text
14
15| fields text, parent_process_name, process, user, dest
16
17| apply detect_suspicious_processnames_using_pretrained_model_in_dsdl
18
19| rename predicted_label as is_suspicious_score
20
21| rename text as process_name
22
23| where is_suspicious_score > 0.5
24
25| `detect_suspicious_processnames_using_pretrained_model_in_dsdl_filter`

Data Source

Name	Platform	Sourcetype	Source
Sysmon EventID 1	Windows	'XmlWinEventLog'	'XmlWinEventLog:Microsoft-Windows-Sysmon/Operational'

Macros Used

Annotations

Default Configuration

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

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

False positives may be present if a suspicious processname is similar to a benign processname.

Associated Analytic Story

• Suspicious Command-Line Executions

References

• https://www.splunk.com/en_us/blog/security/random-words-on-entropy-and-dns.html

• https://www.cisa.gov/uscert/ncas/alerts/aa20-302a

Detection Testing

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: 9