Neural Malware Classification With High-Confidence Explanations
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Solution Overview
Problem
Existing machine learning systems for malware classification, particularly neural networks, lack transparency and confidence in their outputs due to complex coefficients and non-human-readable inputs, making it difficult for human analysts to trust and understand the classification process.
Innovation Solution
A machine learning system using a hierarchical multi-instance-learning neural network trained with a weighting or masking function to generate human-readable explanations of input data subsets responsible for the classification, optimizing the loss function and reducing non-contributory weights to enhance confidence and readability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models like neural networks are used for malware classification, then classification accuracy and productivity are improved, but interpretability and human confidence deteriorate due to the Black Box Verdict problem
Solution Approach 1:
The patent extracts and presents only the most relevant features and their contributions to the classification decision from the complex neural network output. By identifying and isolating the key explanatory factors (such as specific malware characteristics or behavior patterns), the system provides human-readable explanations that highlight what drove the classification without requiring humans to understand the entire complex model architecture.
Solution Approach 2:
The patent introduces an explanation generation module as an intermediary between the neural network and human users. This module translates the abstract neural network decisions into concrete, human-understandable explanations by identifying which input features were most influential and how they contributed to the classification, thereby mediating between the black box model and human interpretability requirements.
2Reliability
If complex neural network models are used for malware classification, then detection accuracy is improved, but device complexity and difficulty of understanding increase
Solution Approach 1:
The patent segments the complex neural network into functional components: the neural network itself for processing, the explanation generation module for interpretability, and the feature selection mechanism for identifying key factors. This segmentation allows the system to maintain complex processing capabilities while presenting a simplified, modular architecture that is easier to understand and maintain.
Solution Approach 2:
Instead of trying to simplify the neural network architecture to improve interpretability, the patent inverts the approach by keeping the complex network for accurate detection and adding a separate explanation generation layer that translates its decisions. This inversion allows the model to achieve high accuracy through complexity while maintaining understandability through the explanatory layer.
3Reliability
If human researchers manually verify malware classifications, then explanation quality and reliability are improved, but time consumption and workload increase
Solution Approach 1:
The patent enables the system to verify and explain its own classifications autonomously. The explanation generation module automatically identifies and presents the key factors that led to each classification decision, allowing the system to self-verify its outputs without requiring extensive manual review. This self-service capability maintains high reliability while dramatically reducing the time humans need to spend on verification.
Solution Approach 2:
The patent implements a feedback mechanism where the explanation generation module continuously refines and improves the quality of explanations based on the classification results. This feedback loop allows the system to learn from previous classifications and improve future explanations, reducing the need for manual verification over time while maintaining high reliability.
Data Source
AI summary
A malware classification system provides improved confidence in explanations of neural network classification outputs using methods such as weighting or masking when training the neural network to train the network on a sample resembling or including the explanation. The explanation in some examples comprises a subset of a hierarchical input vector that is responsible for the neural network's classification output. In another example the neural network has an inner portion configured to reduce the weight of elements of the output not significantly contributing to the explanation of the output, such as by reducing the weight of as many such outputs to zero as is practical in generating the desired output.