Detection: Detect suspicious processnames using pretrained model in DSDL

EXPERIMENTAL DETECTION

This detection status is set to experimental. The Splunk Threat Research team has not yet fully tested, simulated, or built comprehensive datasets for this detection. As such, this analytic is not officially supported. If you have any questions or concerns, please reach out to us at research@splunk.com.

Updated Date: 2024-10-17 ID: a15f8977-ad7d-4669-92ef-b59b97219bf5 Author: Abhinav Mishra, Kumar Sharad and Namratha Sreekanta, Splunk Type: Anomaly Product: Splunk Enterprise Security

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 by Processes.process_name Processes.parent_process_name Processes.process Processes.user Processes.dest 
 3| `drop_dm_object_name(Processes)` 
 4| rename process_name as text 
 5| fields text, parent_process_name, process, user, dest 
 6| apply detect_suspicious_processnames_using_pretrained_model_in_dsdl 
 7| rename predicted_label as is_suspicious_score 
 8| rename text as process_name 
 9| where is_suspicious_score > 0.5 
10| `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

Name	Value
security_content_summariesonly	`summariesonly=`summariesonly_config `allow_old_summaries=`oldsummaries_config `fillnull_value=`fillnull_config``
detect_suspicious_processnames_using_pretrained_model_in_dsdl_filter	`search *`

detect_suspicious_processnames_using_pretrained_model_in_dsdl_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Annotations

- MITRE ATT&CK

+ Kill Chain Phases

+ NIST

+ CIS

- Threat Actors

ID	Technique	Tactic
T1059	Command and Scripting Interpreter	Execution

KillChainPhase.INSTALLATION

NistCategory.DE_AE

Cis18Value.CIS_10

APT19

APT32

APT37

APT39

Dragonfly

FIN5

FIN6

FIN7

Fox Kitten

Ke3chang

OilRig

Saint Bear

Stealth Falcon

Whitefly

Windigo

Winter Vivern

Default Configuration

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

Setting	Value
Disabled	true
Cron Schedule	`0 * * * *`
Earliest Time	`-70m@m`
Latest Time	`-10m@m`
Schedule Window	`auto`
Creates Risk Event	True

This configuration file applies to all detections of type anomaly. These detections will use Risk Based Alerting.

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

Risk Based Analytics (RBA)

Risk Message	Risk Score	Impact	Confidence
The process $process$ is running from an unusual place by $user$ on $dest$ with a processname that appears to be randomly generated.	45	50	90

The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

References

Detection Testing

Test Type	Status	Dataset	Source	Sourcetype
Validation	Not Applicable	N/A	N/A	N/A
Unit	❌ Failing	N/A	`N/A`	`N/A`
Integration	❌ Failing	N/A	`N/A`	`N/A`

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: 3