Detection: Detect suspicious DNS TXT records 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: 92f65c3a-968c-11ed-a1eb-0242ac120002 Author: Abhinav Mishra, Kumar Sharad and Namratha Sreekanta, Splunk Type: Anomaly Product: Splunk Enterprise Security

Description

The following analytic identifies suspicious DNS TXT records using a pre-trained deep learning model. It leverages DNS response data from the Network Resolution data model, categorizing TXT records into known types via regular expressions. Records that do not match known patterns are flagged as suspicious. This activity is significant as DNS TXT records can be used for data exfiltration or command-and-control communication. If confirmed malicious, attackers could use these records to covertly transfer data or receive instructions, posing a severe threat to network security.

 1
 2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type 
 3| `drop_dm_object_name("DNS")` 
 4| rename answer as text 
 5| fields firstTime, lastTime, message_type,record_type,src,dest, text 
 6| apply detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl 
 7| rename predicted_is_unknown as is_suspicious_score 
 8| where is_suspicious_score > 0.5 
 9| `security_content_ctime(firstTime)` 
10| `security_content_ctime(lastTime)` 
11| table src,dest,text,record_type, firstTime, lastTime,is_suspicious_score 
12| `detect_suspicious_dns_txt_records_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_suspicious_dns_txt_records_using_pretrained_model_in_dsdl_filter search *
detect_suspicious_dns_txt_records_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
T1568.002 Domain Generation Algorithms Command And Control
KillChainPhase.COMMAND_AND_CONTROL
NistCategory.DE_AE
Cis18Value.CIS_13
APT41
TA551

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

Steps to deploy detect suspicious DNS TXT records 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_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz.
  • Download the detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.ipynb Jupyter notebook from https://github.com/splunk/security_content/notebooks.
  • Login to the Jupyter Lab assigned for detect_suspicious_dns_txt_records_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_suspicious_dns_txt_records_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_suspicious_dns_txt_records_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_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.ipynb` into Jupyter lab notebooks folder using the upload option in Jupyter lab.
  • Save the notebook using the save option in Jupyter notebook.
  • Upload detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.json into notebooks/data folder.

Known False Positives

False positives may be present if DNS TXT record contents are similar to benign DNS TXT record contents.

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
A suspicious DNS TXT response was detected on host $src$ , kindly review. 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