Neuron-Level Malware Detection in Deployed Neural Network Models
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Solution Overview
Problem
Neural network-based models are vulnerable to sophisticated neuron-level malware attacks that conventional code vulnerability scanning techniques fail to detect effectively.
Innovation Solution
Implement a system that performs bit-level corruption checks, followed by periodic anomaly detection using explainable AI to identify neuron-level alterations caused by malware, and transmits alerts when anomalies are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional code vulnerability scanning techniques are used, then the scanning process is simple and fast, but neuron-level malware attacks cannot be reliably identified
Solution Approach 1:
The vulnerability detection process is segmented into multiple specialized stages: bit-level corruption check, periodic anomaly detection, and explainable AI-based malware exploration. Each stage focuses on specific aspects of malware detection, allowing comprehensive analysis without requiring a single overly complex system
Solution Approach 2:
An intermediary analysis layer is introduced between conventional scanning and final detection. This layer includes anomaly detection mechanisms and explainable AI components that bridge the gap between simple scanning and complex malware identification, enabling reliable neuron-level attack detection
2Speed
If real-time deployment of neural network models is implemented, then service responsiveness is improved, but vulnerability to malware attacks increases
Solution Approach 1:
Security checks are performed in advance before model deployment. The bit-level corruption check and initial anomaly detection are conducted on the model before it is deployed in real-time, ensuring that only verified safe models are deployed while maintaining fast service responsiveness
Solution Approach 2:
A continuous feedback mechanism is implemented through periodic anomaly detection on deployed models. The system continuously monitors real-time model behavior and provides feedback to identify potential malware infections, allowing rapid response while maintaining real-time deployment benefits
3Reliability
If comprehensive security checks are performed on neural network models, then detection reliability is improved, but processing time increases
Solution Approach 1:
Security checks are implemented as periodic operations rather than continuous exhaustive analysis. The system performs bit-level corruption checks at deployment and periodic anomaly detection at scheduled intervals, providing reliable detection while minimizing processing time loss through efficient timing of comprehensive checks
Data Source
AI summary
A method and system for performing a neural level vulnerability detection are disclosed. The method includes receiving a neural network model, performing a bit-level corruption check on the received neural network model indicating a potential presence of an embedded malware in the neural network model, and deploying the neural network model. The method further includes scheduling a periodic anomaly detection on the deployed neural network model, performing the periodic anomaly detection on the deployed neural network model for detecting an anomaly at a scheduled time, and when the anomaly is detected on the neural network model, executing an explainable artificial intelligence based embedded malware exploration to identify an alteration made by the embedded malware at a neuron level of the neural network model.