Hardware-Assisted Malware Detection with Explainable ML
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Solution Overview
Problem
Current malware detection methods, both software-based and hardware-based, face challenges in accurately and transparently identifying malicious behavior due to limitations in interpreting classification results and handling time-sequential data, leading to high false positive rates and misclassification.
Innovation Solution
The integration of explainable machine learning with hardware-assisted malware detection using Recurrent Neural Networks (RNNs) and a penalty term to handle time-sequential data, providing interpretable explanations for classification results by ranking feature contributions and utilizing explainable outcomes to accurately localize malicious behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hardware-assisted malware detection is used, then detection accuracy is improved, but interpretability of classification results deteriorates
Solution Approach 1:
The patent introduces an intermediary explanation module that acts as a bridge between the hardware-assisted detection system and the user. This module generates human-interpretable explanations for classification results by analyzing feature contributions and temporal patterns, without interfering with the core detection accuracy of the hardware system.
Solution Approach 2:
The patent segments the detection process into distinct components: hardware-based feature extraction, machine learning classification, and explanation generation. This segmentation allows each component to specialize - the hardware ensures accuracy while the explanation module provides interpretability, resolving the contradiction between the two requirements.
2Ease of operation
If traditional malware detection methods are used, then ease of operation is maintained, but false positive rate increases
Solution Approach 1:
The patent changes key parameters of the detection system by incorporating temporal features and hardware-based measurements. These parameter changes enable the system to distinguish malicious behavior more accurately, reducing false positives while maintaining ease of operation through automated analysis.
Solution Approach 2:
The patent implements feedback mechanisms where classification results and explanation quality are continuously evaluated. This feedback loop allows the system to adjust detection parameters and improve reliability over time without requiring manual intervention, thus maintaining ease of operation while reducing false positives.
3Loss of information
If explainable machine learning is integrated, then transparency of results is improved, but device complexity increases
Solution Approach 1:
The patent extracts the complexity of explainable AI into a separate, modular explanation generation component. This extraction allows the core detection system to remain relatively simple while providing transparent results through the dedicated explanation module, which can be independently optimized and managed.
4Measurement precision
If hardware features are utilized, then detection capability is improved, but difficulty of detecting and measuring malicious behavior increases
Solution Approach 1:
The patent replaces manual analysis methods with automated machine learning systems that process hardware features. This substitution handles the complexity of detecting and measuring malicious behavior automatically, improving detection capability while reducing the practical difficulty through automation rather than manual intervention.
Data Source
AI summary
The present disclosure describes systems and methods for hardware-assisted malware detection. One such system comprises a memory; and a hardware processor of a computing device operatively coupled to the memory. The hardware processor is configured to execute a software application suspected of being malware; monitor behavior of the software application at run-time; and acquire an input time sequence of data records based on a trace analysis of the software application, wherein the input time sequence comprises a plurality of features of the software application. The hardware processor is further configured to classify the software application as being a malicious software application based on the plurality of features of the software application; and output a ranking of a subset plurality of features by their respective contributions towards the classification of the software application as being malicious software.