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 uses a pre-trained Deep Learning model to predict whether a processname is suspicious or not. Malwares and malicious programs such as ransomware often use tactics, techniques, and procedures (TTPs) such as copying malicious files to the local machine to propagate themselves across the network. A key indicator of compromise is that after a successful execution of the malware, it copies itself as an executable file with a randomly generated filename and places this file in one of the directories. Such techniques are seen in several malwares such as TrickBot. We develop machine learning model that uses a Recurrent Neural Network (RNN) to distinguish between malicious and benign processnames. The model is trained independently and is then made available for download. We use a character level RNN to classify malicious vs. benign processnames. The higher is_malicious_prob, the more likely is the processname to be suspicious (between [0,1]). The threshold for flagging a processname as suspicious is set as 0.5.
- Type: Anomaly
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2023-01-23
- Author: Abhinav Mishra, Kumar Sharad and Namratha Sreekanta, Splunk
- ID: a15f8977-ad7d-4669-92ef-b59b97219bf5
Kill Chain Phase
- CIS 10
1 2 3 4 5 6 7 8 9 10 | 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 | `drop_dm_object_name(Processes)` | rename process_name as text | fields text, parent_process_name, process, user, dest | apply detect_suspicious_processnames_using_pretrained_model_in_dsdl | rename predicted_label as is_suspicious_score | rename text as process_name | where is_suspicious_score > 0.5 | `detect_suspicious_processnames_using_pretrained_model_in_dsdl_filter`
The SPL above uses the following Macros:
detect_suspicious_processnames_using_pretrained_model_in_dsdl_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 may be present if a suspicious processname is similar to a benign processname.
Associated Analytic Story
|45.0||50||90||The process $process$ is running from an unusual place by $user$ on $dest$ with a processname that appears to be randomly generated.|
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