ID | Technique | Tactic |
---|---|---|
T1568.002 | Domain Generation Algorithms | Command And Control |
Detection: Detect DGA domains 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.
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
Name | Platform | Sourcetype | Source | Supported App |
---|---|---|---|---|
N/A | N/A | N/A | N/A | N/A |
Macros Used
Name | Value |
---|---|
security_content_ctime | convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$) |
detect_dga_domains_using_pretrained_model_in_dsdl_filter | search * |
detect_dga_domains_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
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 |
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
usingtar -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
intonotebooks/data
folder.
Known False Positives
False positives may be present if domain name is similar to dga generated domains.
Associated Analytic Story
Risk Based Analytics (RBA)
Risk Message | Risk Score | Impact | Confidence |
---|---|---|---|
A potential connection to a DGA domain $domain$ was detected from host $src$, kindly review. | 63 | 70 | 90 |
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: 2