Neural Network Autoencoder for Malware Feature Vector Classification
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Solution Overview
Problem
Existing systems face challenges in efficiently identifying and processing characteristics of potential malware files to determine their type and representation, as different characteristics may be more relevant for different malware threats, and representing these characteristics in a useful format can be resource-intensive.
Innovation Solution
A processor-based system that extracts feature vectors from potential malware files, uses a trained neural network autoencoder to produce modified feature vectors, introduces Gaussian noise to ensure a Gaussian distribution, and classifies the files using a decoder neural network to identify associated classes and perform remedial actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple characteristics are extracted and compared to identify different malware threats, then the accuracy of malware identification is improved, but the resource consumption and processing complexity increase significantly
Solution Approach 1:
The patent extracts only the most relevant characteristics from malware files by using a trained neural network autoencoder to process feature vectors. The autoencoder learns to extract and retain only the essential features needed for accurate malware identification, filtering out redundant or less important characteristics. This selective extraction maintains high identification accuracy while significantly reducing processing resources required compared to analyzing all possible characteristics.
Solution Approach 2:
The system performs preliminary action by pre-training the neural network autoencoder and decoder on known malware datasets before actual malware identification tasks. This pre-training phase allows the system to learn optimal feature representations and classification boundaries in advance, so that during runtime, only efficient inference is needed without requiring complex real-time analysis of all possible characteristics.
2Adaptability or versatility
If characteristics are represented in different formats to match various malware types, then the versatility of malware detection is improved, but the complexity of determining the best representation increases
Solution Approach 1:
The patent implements universality by using a single neural network autoencoder that can process and represent different malware characteristics in a unified feature space. The autoencoder learns to handle multiple types of features (strings, numbers, binary data, etc.) and transform them into a common representation format that can be consistently processed. This eliminates the need for separate processing pipelines for different feature types, reducing overall system complexity while maintaining broad malware type coverage.
Solution Approach 2:
The neural network autoencoder acts as an intermediary that translates diverse characteristic representations into a standardized internal representation format. Instead of requiring the system to directly handle multiple complex representation formats simultaneously, the autoencoder mediates by converting all input features into a unified latent space representation, simplifying the overall system architecture while preserving adaptability to various malware types.
3Reliability
If a comprehensive set of characteristics is analyzed to ensure accurate malware classification, then the reliability of malware detection is improved, but the time required for analysis increases
Solution Approach 1:
The system extracts only the most discriminative characteristics by using the pre-trained autoencoder that has learned which features are most important for malware classification. The autoencoder automatically performs feature selection by focusing computational resources on the most informative latent dimensions, eliminating the need to manually analyze every possible characteristic and significantly reducing analysis time while maintaining high detection reliability.
Solution Approach 2:
The pre-training phase performs preliminary action by learning optimal feature importance and representation during the training period using known malware datasets. This allows the system to make intelligent guesses about which features are most relevant during actual detection tasks, avoiding the need to exhaustively analyze all possible characteristics in real-time while maintaining reliable detection performance.
Data Source
AI summary
In some embodiments, an apparatus includes a memory and a processor operatively coupled to the memory. The processor is configured to identify a feature vector for a potentially malicious file and provide the feature vector as an input to a trained neural network autoencoder to produce a modified feature vector. The processor is configured to generate an output vector by introducing Gaussian noise into the modified feature vector to ensure a Gaussian distribution for the output vector within a set of modified feature vectors. The processor is configured to provide the output vector as an input to a trained neural network decoder associated with the trained neural network autoencoder to produce an identifier of a class associated with the set of modified feature vectors. The processor is configured to perform a remedial action on the potentially malicious file based on the potentially malicious file being associated with the class.