Transformer Attention Malware Rules With Human Feedback
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Solution Overview
Problem
Existing malware detection systems face challenges with high costs, inefficiency, and lack of transparency due to manual rule creation and reliance on unverifiable AI, leading to false positives and negatives.
Innovation Solution
A semi-automated approach using an AI transformer model for behavior sequence classification, combined with backpropagation algorithms, to generate transparent malware detection rules with human oversight, reducing resource consumption and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual rule creation by human analysts is used, then transparency and precision are maintained, but productivity and scalability deteriorate due to the huge amount of new malware samples
Solution Approach 1:
The patent introduces an AI model as an intermediary between malware samples and detection rules. The AI model automatically analyzes malware behavior sequences and generates detection rules, acting as a mediator that bridges the gap between manual analysis precision and automated processing speed. This allows the system to handle large volumes of malware samples while maintaining detection quality through the AI's learned patterns.
Solution Approach 2:
The patent implements a feedback mechanism where human analysts review and adjust AI-generated rules. The system continuously learns from human corrections, refining its rule generation capability over time. This feedback loop ensures that automation progressively improves while maintaining the precision that human analysts provide, resolving the contradiction between automated speed and human precision.
2Productivity
If AI models are used for malware detection, then productivity and scalability improve, but transparency deteriorates creating a black-box system that is hard to understand
Solution Approach 1:
The patent extracts and highlights the crucial parts of behavior sequences that lead the AI model to reach its verdict. By identifying and presenting these key behavioral patterns, the system makes the AI's decision-making process transparent and understandable to human analysts, allowing them to verify and trust the automated detection rules without sacrificing analysis speed.
3Reliability
If complex AI models like LSTM or RNN are used, then detection accuracy improves, but device complexity and resource consumption increase
Solution Approach 1:
The patent employs a transformer-based AI model that, while accurate, is designed to be computationally efficient compared to traditional LSTM or RNN architectures. The model generates detection rules during training that can then be executed by simple behavior monitoring systems without requiring the complex AI model at runtime. This allows high accuracy during rule generation while keeping the deployed detection system lightweight and resource-efficient.
4Productivity
If purely automated AI systems are used, then productivity improves, but reliability deteriorates due to false positives and negatives without human verification
Solution Approach 1:
The patent implements a feedback mechanism where human analysts review and adjust AI-generated rules. The system continuously learns from human corrections, refining its rule generation capability over time. This feedback loop ensures that automation progressively improves while maintaining the precision that human analysts provide, resolving the contradiction between automated speed and human precision.
Data Source
AI summary
Systems and methods implement artificial intelligence to automatically generate malware detection rules. In a first phase, an AI model is trained on large amounts of data, so the AI model can learn to distinguish between benign applications and different malware families. In a second phase, the AI model is queried with new malware samples, and systems and methods propose new malware detection rules for those samples.