Malware Classification via Autoencoder Feature Space
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Solution Overview
Problem
Traditional malware detection systems face inefficiencies due to the increasing complexity of malware, with 65% of malware being packed or polymorphic, leading to time-consuming manual processes in dynamic analysis, and current systems often focus on single aspects of dynamic traces, requiring heavy manual effort for pattern discovery.
Innovation Solution
A method involving computing devices that perform mappings of dynamic analysis traces to vector representations, using autoencoders to generate feature space representations that correlate identified features with malware classes, incorporating both ordered and unordered event analyses for comprehensive classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dynamic analysis is performed manually to classify malware, then classification accuracy is improved, but time consumption increases significantly
Solution Approach 1:
The system performs automatic malware classification through self-service mechanisms. The autoencoder model automatically processes dynamic traces, extracts features, and classifies malware without requiring manual analyst intervention. The system trains itself on labeled malware data and autonomously generates classification results, eliminating the time-consuming manual observation and classification process while maintaining high accuracy through automated feature extraction and pattern recognition.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computational system. Instead of human analysts manually examining dynamic traces and making classification decisions, the system uses machine learning models (autoencoders, neural networks) to automatically process traces, extract features, and perform classification. This substitution of mechanical human labor with automated computational mechanisms resolves the contradiction between accuracy and time consumption.
2Device complexity
If traditional dynamic analysis systems focus on single aspects of traces, then system complexity is reduced, but classification accuracy deteriorates
Solution Approach 1:
The system merges multiple aspects of dynamic trace analysis into a unified comprehensive model. Instead of analyzing single aspects separately, the patent combines ordered event sequences, unordered event patterns, function call traces, and various behavioral features into an integrated analysis framework. The autoencoder model processes multiple trace dimensions simultaneously, merging diverse information sources to achieve accurate classification while managing complexity through unified architectural design.
Solution Approach 2:
The patent implements a universal analysis system that handles multiple types of dynamic trace information through a single multi-functional platform. The system can process ordered events, unordered events, function calls, and various other trace aspects using the same underlying architecture and machine learning models. This multi-functionality allows comprehensive analysis without proportionally increasing system complexity, as the same core infrastructure serves multiple analysis purposes.
3Measurement precision
If manual pattern discovery is performed to identify dynamic analysis patterns, then pattern accuracy is improved, but manual effort increases heavily
Solution Approach 1:
The system performs self-service pattern discovery through automated machine learning. Instead of requiring security analysts to manually reverse-engineer malware behavior and discover patterns, the autoencoder model automatically learns relevant patterns from labeled malware data during training. The system autonomously identifies important features, event sequences, and behavioral patterns that distinguish malware classes, eliminating heavy manual effort while maintaining pattern accuracy through data-driven learning.
Solution Approach 2:
The patent transforms the pattern discovery process from a manual parameter-tuning exercise to an automated learning process. Instead of analysts manually adjusting analysis parameters and discovering patterns through experience, the system uses machine learning algorithms that automatically optimize feature extraction parameters, event weighting, and pattern recognition thresholds based on training data. This parameter transformation from manual to automated optimization resolves the contradiction between pattern accuracy and manual effort.
Data Source
AI summary
The disclosed computer-implemented method for malware classification may include receiving dynamic analysis traces that include event descriptions regarding malware programs, and labels regarding classes of malware programs; performing a first mapping of the event descriptions to a first set of vector representations, wherein order of the events is not taken into account by the first mapping; performing a second mapping of the event descriptions to a second set of vector representations, wherein order of the events is taken into account by the second mapping; combining the first set of vector representations and the second set of vector representations into a combined set of vector representations; inputting the combined set of vector representations, along with the labels, into an autoencoder; and training the autoencoder to generate a feature space representation that correlates identified features with classes of malware. Various other methods, systems, and computer-readable media are also disclosed.