Structure-Aware Neural Networks for Adaptive Malware Detection
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Solution Overview
Problem
Existing malware detection methods using machine learning models face challenges due to the high cost and inefficiency of human-defined features and the naive treatment of binary code as a homogenous sequence of bytes, limiting their effectiveness against evolving malware variants.
Innovation Solution
A structure-aware neural network system that parses computer files into portions based on their internal structure, using class-specific machine-learned feature extractors and prediction models to generate embeddings and predictions, enhancing detection accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hand-crafted features are used for malware detection, then detection accuracy can be improved for known malware, but the cost and time required to create and maintain features increases significantly
Solution Approach 1:
The system enables automatic feature extraction and model training without requiring manual feature engineering. The machine learning model automatically learns relevant features from raw binary data, eliminating the need for cybersecurity experts to manually create and maintain feature definitions, thus resolving the contradiction between detection accuracy and feature maintenance time
Solution Approach 2:
The patent replaces the manual mechanical process of feature crafting by experts with an automated machine learning system. The ML model automatically processes raw binary data and extracts meaningful features, substituting the time-consuming human effort with an efficient computational process that maintains high detection accuracy
2Ease of operation
If binary code is treated as a homogenous sequence of bytes, then processing simplicity is improved, but detection effectiveness against evolving malware variants deteriorates
Solution Approach 1:
The patent segments the binary code into meaningful structural components (headers, sections, functions, etc.) rather than treating it as a homogenous byte sequence. This segmentation allows the model to process each component appropriately while maintaining overall simplicity, and enables better adaptation to malware variants by capturing structural patterns that persist across different malware versions
Solution Approach 2:
The system applies different processing approaches to different parts of the binary code based on their specific characteristics. Rather than uniform processing, the model adapts its analysis to local structural features such as code sections, data segments, and control flow patterns, improving both processing efficiency and adaptability to various malware types
Data Source
AI summary
Provided is a malware detection system that provides structure-aware neural networks for performing malware detection. In particular, rather than treat the entire computer file as one large input to a deep neural network, the malware detection system can break the file up based on the internal file structure. Each portion of the computer file can then be processed using individual neural networks and the outputs of these networks can be combined and similarly processed. In this way the overall system can evaluate the file with knowledge of the structure of the file, enabling the malware detection to have a higher-order understanding of the interoperation of different portions of the computer file.