Dictionary DGA Detection via Deep Learning Embeddings
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Solution Overview
Problem
Conventional methods for detecting dictionary domain generation algorithm (DGA) domain names are inaccurate and lack the resources to handle high-volume requests effectively, particularly in network security contexts where dictionary DGA domain names are difficult to identify as malicious due to their resemblance to authentic names.
Innovation Solution
A deep learning model comprising a Long Short-Term Memory (LSTM) network, Convolutional Neural Network (CNN), and multilayer perceptron (MLP) is used to generate dense embedding vectors and provide a dictionary DGA score for suspect domain names, improving detection accuracy and scalability by learning relationships among features without requiring manually selected features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional detection methods (reverse engineering, blacklisting) are used to detect dictionary DGA domain names, then detection capability is provided, but accuracy is insufficient and the rate of detection cannot keep pace with the rate of new malicious domain generation
Solution Approach 1:
The patent replaces manual reverse engineering and static blacklist maintenance with an automated machine learning system. The system uses trained models to automatically analyze domain names and predict their maliciousness, substituting human-driven mechanical processes with automated computational analysis that can scale to handle high volumes of domain names in real-time.
Solution Approach 2:
The patent performs preliminary actions by pre-training detection models on historical malware data and maintaining updated blacklists in advance. This allows the system to be prepared for new threats before they fully propagate, enabling faster response times and more accurate detection when new dictionary DGA domain names are generated.
2Adaptability or versatility
If dictionary DGA domain names are used by malware, then malware can communicate with command and control networks, but the domain names become difficult for human users and conventional models to identify as malicious
Solution Approach 1:
The patent replaces human user judgment and conventional detection models with machine learning-based automated detection systems. These systems can identify subtle patterns in dictionary DGA domain names that are imperceptible to humans, analyzing linguistic features, domain structures, and contextual information to detect maliciousness despite the domains' authentic appearance.
Solution Approach 2:
The patent changes the detection parameters from simple blacklist matching to multi-dimensional analysis including domain name length, character distribution, linguistic features, and contextual metadata. By analyzing multiple parameters simultaneously, the system can distinguish malicious dictionary DGA domains from legitimate domains that may share similar surface characteristics.
3Adaptability or versatility
If new DGA domain names are generated in bulk by malware, then malware can evade detection, but the volume of generated domains outpaces the rate at which blacklists can grow
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models on extensive historical malware data before deployment. This allows the system to recognize patterns from previously observed malware families and apply this knowledge to detect new dictionary DGA variants, enabling the detection rate to keep pace with or exceed the generation rate of new malicious domains.
Solution Approach 2:
The patent replaces the manual blacklist update process with automated machine learning-based detection that can analyze and respond to new threats in real-time. The system continuously learns from new data and adapts its detection criteria, eliminating the bottleneck of manual blacklist maintenance and enabling scalable detection of high-volume DGA domain generation.
Data Source
AI summary
Systems and methods are provided for detecting dictionary domain generation algorithm domain names using deep learning models. The system and method may comprise training and applying a model comprising a long short-term memory network, a convolutional neural network, and a feed forward neural network that accepts as input an output from the long short-term memory network and convolutional neural network. The system and method may provide a score indicating the likelihood that a domain name was generated using a dictionary domain generation algorithm domain name. The system and method may be provided as a service.