Explainable Malware Detection Model Resolving Accuracy Interpretability Trade-off
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Solution Overview
Problem
Existing malware detection methods struggle with providing clear, actionable explanations for malicious file detection, especially when dealing with complex executable data, and often rely on either global or local explanations without considering the hybrid approach needed for comprehensive understanding.
Innovation Solution
The Hybrid and Hierarchical Explainable (H2X) model combines global explanations with localized details, using attention mechanisms and Shapley Additive Explanations (SHAP) analysis to provide comprehensive explanations for malware behavior, and employs the Binary Search Explanation (BSX) technique to reduce feature dimensionality and enhance explanation sparsity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning-based techniques are used for automated malware analysis, then detection accuracy is improved, but interpretability and explainability deteriorate due to black-box nature
Solution Approach 1:
The patent introduces an explainability module as an intermediary between the deep learning detector and the user. This module includes attention mechanisms that highlight important features and generate natural language explanations, mediating the black-box predictions into interpretable insights without altering the core detection accuracy of the deep learning model.
Solution Approach 2:
The system segments the malware analysis process into distinct components: a deep learning detector for accurate classification and an separate explainability module for interpretation. This segmentation allows each component to specialize - the detector focuses on accuracy while the explanation module focuses on interpretability, resolving the contradiction between these two requirements.
2Loss of information
If global explanations are used for malware detection, then overall model behavior is explained, but local specificities and nuanced details are lost
Solution Approach 1:
The patent implements local quality by providing different explanation granularities for different needs. The attention mechanisms can operate at multiple levels - globally across the entire malware sample and locally on specific features or code segments. This allows the system to adapt the explanation scope to match the specific analytical requirement, whether global overview or local detail is needed.
Solution Approach 2:
The system adds a new dimension to explanations by incorporating both global and local perspectives simultaneously through multi-level attention mechanisms. Rather than choosing between global or local explanations, the patent creates a hierarchical explanation structure that operates across multiple dimensions of analysis, from overall patterns down to specific suspicious features.
3Measurement precision
If local explanations are used for malware detection, then specific features are highlighted, but overall context and global patterns are lost
Solution Approach 1:
The patent merges global and local explanation approaches into a unified system. The attention mechanisms combine global attention (considering the entire malware sample) with local attention (focusing on specific features), allowing the system to simultaneously provide both broad contextual understanding and detailed feature-level analysis in an integrated manner.
4Measurement precision
If comprehensive feature analysis is performed for malware detection, then detection accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies partial action by using attention mechanisms to selectively focus computational resources on the most relevant features rather than analyzing all features equally. The attention scores identify and prioritize critical features, allowing the system to achieve high detection accuracy by concentrating analysis on a subset of the most important indicators rather than exhaustively processing every feature.
Solution Approach 2:
The system dynamically changes parameters during analysis based on attention scores. Features are weighted and prioritized according to their relevance, with computational resources allocated proportionally. This parameter-based resource allocation allows comprehensive analysis where needed while reducing complexity for less critical features, balancing accuracy and computational cost.
Data Source
AI summary
Systems, methods, and software can be used to determine risks of software files. In some aspects, a method includes: obtaining an input, wherein the input comprises a binary file; determining a second set of feature vectors of the input; performing a canonical correlation analysis (CCA) on the second set of feature vectors and a first set of feature vectors to obtain a first vector and a second vector; calculating a correlation coefficient value of the first vector and the second vector; obtaining a third set of feature vectors based on the correlation coefficient value; and providing, based on the third set of feature vectors, information indicating a level of a security risk of the input and information indicating features associated with the security risk of the input.