AI File Maliciousness Classification With Reasoning Explanations
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Solution Overview
Problem
Signature-based antivirus approaches struggle to detect novel malware, and deep learning techniques lack transparency, making them ineffective in safety-critical scenarios that require accountability and scalability.
Innovation Solution
An AI model, such as an artificial general intelligence model, classifies files as malicious or non-malicious while providing explanations for its reasoning, reducing the need for human intervention and enhancing transparency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning techniques are used for malware classification, then classification accuracy is improved, but transparency and interpretability deteriorate
Solution Approach 1:
The patent introduces an intermediary explanation module that mediates between the deep learning classifier and the user. This module generates human-interpretable explanations (e.g., highlighting suspicious code patterns, describing behavioral characteristics) that bridge the gap between the black-box model and human understanding, allowing users to comprehend the classification rationale without compromising the model's accuracy.
Solution Approach 2:
The system segments the classification process into distinct components: the deep learning model for accurate classification and a separate explanation generation component for transparency. By dividing the system into these functional modules, the patent maintains the high accuracy of deep learning while adding an independent layer that provides interpretable insights into the classification decisions.
2Reliability
If human experts are used for file classification, then accountability is improved, but scalability deteriorates
Solution Approach 1:
The system enables automated self-service classification through the AI model, which independently analyzes files and generates both classifications and explanations without requiring human intervention for each file. This allows the system to handle large volumes of files autonomously while maintaining accountability through the generated explanations that document the reasoning process.
Solution Approach 2:
The system implements feedback mechanisms where the AI model's explanations are continuously refined based on user interactions and validation. This allows the automated system to maintain high accountability by learning from human feedback while preserving scalability, as the model improves its explanation quality without requiring proportional increases in human expert involvement.
3Adaptability or versatility
If traditional machine learning approaches are used, then detection of polymorphic malware is improved, but time consumption deteriorates
Solution Approach 1:
The system performs preliminary feature extraction and representation learning during the training phase, creating compact file embeddings that capture polymorphic characteristics. This preliminary processing enables the model to quickly classify new polymorphic malware without requiring extensive real-time analysis, significantly reducing time consumption while maintaining detection capability.
Solution Approach 2:
The patent transforms the classification problem by changing parameters from traditional hand-crafted features to deep learning-based feature representations. This parameter transformation allows the system to detect polymorphic malware through learned patterns in the data, achieving both high adaptability to variations and fast classification speed through efficient neural network inference.
Data Source
AI summary
Techniques are described herein that are capable of performing AI-based maliciousness classification with an explanation of reasoning. An AI model is caused to classify a file in a malicious class or a non-malicious class and to provide an explanation that describes reasoning used by the AI model for the classification by providing an AI prompt together with the file as inputs to the AI model. The AI prompt requests that the AI model choose between the malicious class and the non-malicious class and further requests the explanation. A response to the AI prompt is received from the AI model. The response includes an indication of a selected class in which the AI model classifies the file and further includes the explanation. The explanation is stored and/or is provided to the user via a user interface, and a security operation is performed with regard to the file.