DNS Exfiltration Detection Using Tokenized Neural Network Vectors
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Solution Overview
Problem
Existing detection methods for DNS exfiltration are inefficient due to the variable length of DNS requests, which complicates the use of machine learning techniques like neural networks, leading to inaccurate predictions and high false positive rates.
Innovation Solution
Tokenize DNS requests into a character-based vector and combine with additional features such as length, entropy, and average values to create an input vector for a neural network, trained to accurately detect DNS exfiltration likelihood, using a concatenation approach to maintain request integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional detection methods are used for DNS exfiltration, then the system can process DNS requests, but the detection accuracy is low and false positive rate is high due to variable length of DNS requests
Solution Approach 1:
The patent transforms the variable-length DNS request data into a fixed-length numerical vector representation by extracting specific parameters (domain length, subdomain count, entropy values, etc.) and normalizing them to a consistent format. This parameter transformation enables machine learning models to process the data effectively while maintaining detection accuracy and reducing false positives.
Solution Approach 2:
The patent replaces traditional rule-based detection mechanisms with machine learning models (neural networks, random forests, gradient boosting) that can automatically learn patterns from the transformed numerical data. This substitution allows the system to adapt to varying DNS request formats without relying on rigid mechanical detection rules, thereby improving accuracy and reducing false positives.
2Reliability
If machine learning techniques are applied to detect DNS exfiltration, then detection capability is enhanced, but the complexity of processing variable length DNS requests increases
Solution Approach 1:
The patent segments the DNS request processing into distinct stages: data extraction (obtaining domain name, subdomains, entropy), data transformation (converting to numerical vectors), and model prediction (applying machine learning). This segmentation simplifies the overall complexity by breaking down the variable-length processing challenge into manageable fixed-length numerical transformations at each stage.
Solution Approach 2:
The patent applies parameter changes by converting complex variable-length DNS request strings into fixed-length numerical vectors through feature extraction (domain length, subdomain count, entropy metrics). This parameter transformation reduces processing complexity for machine learning models while preserving the essential detection capabilities needed to identify exfiltration patterns.
3Productivity
If DNS protocol communications are allowed to move through firewalls without scrutiny, then network communication efficiency is maintained, but malicious DNS tunneling can occur
Solution Approach 1:
The patent introduces an intermediary detection layer that sits between the firewall and the DNS traffic flow. This intermediary automatically analyzes DNS requests using machine learning models without requiring full firewall inspection, thereby maintaining network communication efficiency while providing automated detection of malicious DNS tunneling attempts.
Solution Approach 2:
The patent implements a self-service detection system where the machine learning models automatically analyze and classify DNS requests without requiring manual firewall rule configuration or intervention. The system self-adapts to new threats by learning from training data, maintaining communication efficiency while providing continuous protection against malicious DNS tunneling.
Data Source
AI summary
Methods and devices are disclosed herein to facilitate the detection of Domain Name System (DNS) exfiltration attacks. In some examples, a DNS request is used to generate a tokenized vector that corresponds to the DNS request, features of the DNS request, and aggregated features calculated over a sliding window representative of a recent history of events between a particular source and domain. The tokenized vector is input into a neural network to generate a probability score indicating a likelihood that the current DNS request corresponds to a DNS exfiltration. A graphical user interface is generated to display an indication of the probability score for the current DNS request.