DGA Domain Name Detection via ImageNet Pre-trained Neural Networks
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Solution Overview
Problem
Current methods for detecting Domain Generation Algorithm (DGA) domain names are inefficient due to excessive reliance on artificial characteristic engineering, low detection rates, high false alarm rates, and inability to achieve real-time detection, especially with the rapid generation of thousands of domain names by DGA algorithms.
Innovation Solution
The method involves converting original domain names into multi-dimensional numeric vectors and inputting them into a pre-trained deep learning model based on the ImageNet dataset to generate domain name characteristics, which are then used to train a classifier for real-time DGA domain name detection, leveraging pre-trained models like AlexNet, VGG, and ResNet to avoid intensive training and parameter adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classic statistical characteristic-based method is used for DGA domain name detection, then detection capability is provided, but detection rate is low and false alarm rate is high
Solution Approach 1:
The patent replaces the traditional mechanical approach of manual statistical characteristic engineering with a neural network-based automatic feature extraction system. The neural network automatically learns and extracts relevant features from domain name data, eliminating the need for manual feature engineering and significantly improving detection accuracy while reducing false alarms.
Solution Approach 2:
The neural network performs self-learning and automatic feature extraction from the training data without requiring manual intervention for feature engineering. The system automatically adapts to learn the characteristics of DGA domain names through training, enabling it to improve its detection capability autonomously.
2Reliability
If artificial characteristic engineering is used, then detection method is established, but excessive reliance on manual engineering makes it difficult to achieve and slow
Solution Approach 1:
The patent substitutes manual characteristic engineering with an automated neural network system that performs feature extraction automatically. This replacement eliminates the time-consuming manual process and enables real-time detection by automatically learning and extracting features from domain name data without human intervention.
Solution Approach 2:
The neural network is pre-trained on a large dataset of domain names, performing preliminary learning of DGA characteristics before actual detection. This preliminary training action enables the system to quickly and accurately detect DGA domain names in real-time without requiring manual feature engineering during the detection phase.
3Adaptability or versatility
If DGA generates thousands of domain names rapidly, then evasion capability is enhanced, but collection and updating of blacklists becomes impossible to maintain
Solution Approach 1:
The neural network is pre-trained on a comprehensive dataset that includes numerous DGA domain name samples, performing preliminary learning of various DGA patterns and characteristics. This preliminary training enables the system to recognize and detect new DGA-generated domain names rapidly without requiring continuous manual blacklist updates.
Solution Approach 2:
The system performs self-learning and automatic adaptation to new DGA patterns through continuous training on updated data. The neural network automatically updates its internal representations and detection capabilities without requiring manual blacklist maintenance, enabling it to keep pace with rapidly evolving DGA techniques.
Data Source
AI summary
A real-time detection method and apparatus for DGA domain name. An original domain name is translated into a multi-dimensional numeric vector, the multi-dimensional numeric vector is input into a deep learning model pre-trained based on an ImageNet data set, to generate a domain name feature, a domain name classifier is trained based on the generated domain name feature, and a DGA domain name is classified and predicted based on the domain name classifier obtained by training. The method firstly uses a deep learning model pre-trained based on an ImageNet data set, from the field of visual image classification and detection, for real-time detection of a DGA domain name, avoiding the process of high-intensity training and parameter weight adjustment for the deep learning model in DGA domain name detection. The detection rate is higher, and detection speed is faster.