Detection: Detect DGA domains using pretrained model in DSDL

Description

The following analytic identifies Domain Generation Algorithm (DGA) generated domains using a pre-trained deep learning model. It leverages the Network Resolution data model to analyze domain names and detect unusual character sequences indicative of DGA activity. This behavior is significant as adversaries often use DGAs to generate numerous domain names for command-and-control servers, making it harder to block malicious traffic. If confirmed malicious, this activity could enable attackers to maintain persistent communication with compromised systems, evade detection, and execute further malicious actions.

Search

 1
 2| tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime  max(_time) as lastTime from datamodel=Network_Resolution by DNS.src, DNS.query 
 3| `drop_dm_object_name(DNS)` 
 4| rename query AS domain 
 5| fields IPs, src, domain, firstTime, lastTime 
 6| apply pretrained_dga_model_dsdl 
 7| rename pred_dga_proba AS dga_score 
 8| where dga_score>0.5 
 9| `security_content_ctime(firstTime)`  
10| `security_content_ctime(lastTime)` 
11| table src, domain, IPs, firstTime, lastTime, dga_score 
12| `detect_dga_domains_using_pretrained_model_in_dsdl_filter`

Data Source

Macros Used

Annotations

Default Configuration

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

Implementation

Steps to deploy DGA detection model into Splunk App DSDL.\ This detection depends on the Splunk app for Data Science and Deep Learning which can be found here - https://splunkbase.splunk.com/app/4607/ and the Network Resolution datamodel which can be found here - https://splunkbase.splunk.com/app/1621/. The detection uses a pre-trained deep learning model that needs to be deployed in DSDL app. Follow the steps for deployment here - https://github.com/splunk/security_content/wiki/How-to-deploy-pre-trained-Deep-Learning-models-for-ESCU. * Download the artifacts .tar.gz file from the link https://seal.splunkresearch.com/pretrained_dga_model_dsdl.tar.gz

• Download the pretrained_dga_model_dsdl.ipynb Jupyter notebook from https://github.com/splunk/security_content/notebooks
• Login to the Jupyter Lab for pretrained_dga_model_dsdl container. This container should be listed on Containers page for DSDL app.
• Below steps need to be followed inside Jupyter lab
• Upload the pretrained_dga_model_dsdl.tar.gz file into app/model/data path using the upload option in the jupyter notebook.
• Untar the artifact pretrained_dga_model_dsdl.tar.gz using tar -xf app/model/data/pretrained_dga_model_dsdl.tar.gz -C app/model/data
• Upload pretrained_dga_model_dsdl.pynb into Jupyter lab notebooks folder using the upload option in Jupyter lab
• Save the notebook using the save option in jupyter notebook.
• Upload pretrained_dga_model_dsdl.json into notebooks/data folder.

Known False Positives

False positives may be present if domain name is similar to dga generated domains.

Associated Analytic Story

• Command And Control

• DNS Hijacking

• Data Exfiltration

• Dynamic DNS

• Suspicious DNS Traffic

Risk Based Analytics (RBA)

References

• https://attack.mitre.org/techniques/T1568/002/

• https://unit42.paloaltonetworks.com/threat-brief-understanding-domain-generation-algorithms-dga/

• https://en.wikipedia.org/wiki/Domain_generation_algorithm

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: 2