ID | Technique | Tactic |
---|---|---|
T1048.003 | Exfiltration Over Unencrypted Non-C2 Protocol | Exfiltration |
Detection: Detect DNS Data Exfiltration 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 potential DNS data exfiltration using a pre-trained deep learning model. It leverages DNS request data from the Network Resolution datamodel and computes features from past events between the same source and domain. The model generates a probability score (pred_is_exfiltration_proba) indicating the likelihood of data exfiltration. This activity is significant as DNS tunneling can be used by attackers to covertly exfiltrate sensitive data. If confirmed malicious, this could lead to unauthorized data access and potential data breaches, compromising the organization's security posture.
Search
1
2| tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.src _time DNS.query
3| `drop_dm_object_name("DNS")`
4| sort - _time,src, query
5| streamstats count as rank by src query
6| where rank < 10
7| table src,query,rank,_time
8| apply detect_dns_data_exfiltration_using_pretrained_model_in_dsdl
9| table src,_time,query,rank,pred_is_dns_data_exfiltration_proba,pred_is_dns_data_exfiltration
10| where rank == 1
11| rename pred_is_dns_data_exfiltration_proba as is_exfiltration_score
12| rename pred_is_dns_data_exfiltration as is_exfiltration
13| where is_exfiltration_score > 0.5
14| `security_content_ctime(_time)`
15| table src, _time,query,is_exfiltration_score,is_exfiltration
16| `detect_dns_data_exfiltration_using_pretrained_model_in_dsdl_filter`
Data Source
No data sources specified for this detection.
Macros Used
Name | Value |
---|---|
security_content_ctime | convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$) |
detect_dns_data_exfiltration_using_pretrained_model_in_dsdl_filter | search * |
detect_dns_data_exfiltration_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 detect DNS data exfiltration 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/detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.tar.gz Download thedetect_dns_data_exfiltration_using_pretrained_model_in_dsdl.ipynb
Jupyter notebook from https://github.com/splunk/security_content/notebooks - Login to the Jupyter Lab assigned for detect_dns_data_exfiltration_using_pretrained_model_in_dsdl container. This container should be listed on Containers page for DSDL app.
- Below steps need to be followed inside Jupyter lab
- Upload the detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.tar.gz file into
app/model/data
path using the upload option in the jupyter notebook. - Untar the artifact detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.tar.gz using
tar -xf app/model/data/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz -C app/model/data
- Upload detect_dns_data_exfiltration_using_pretrained_model_in_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
detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.json
intonotebooks/data
folder.
Known False Positives
False positives may be present if DNS data exfiltration request look very similar to benign DNS requests.
Associated Analytic Story
Risk Based Analytics (RBA)
Risk Message | Risk Score | Impact | Confidence |
---|---|---|---|
A DNS data exfiltration request was sent by this host $src$ , kindly review. | 45 | 50 | 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: 3