IC Graph Modeling for Hardware Trojan and IP Piracy Detection
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Solution Overview
Problem
Current IC design and verification methods are inadequate in addressing hardware security threats such as IP piracy and hardware Trojans, particularly due to the stealthy nature of these threats and the limitations of existing detection methods, which are prone to attacks and require significant overhead, are not scalable, and fail to detect unknown threats.
Innovation Solution
A novel methodology using Graph Neural Networks (GNN) to model Integrated Circuits (ICs) as graphs, enabling automated detection and localization of hardware Trojans and IP piracy without additional hardware overhead, by converting circuit designs into graph representations and applying GNNs for feature extraction and threat detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If watermarking and fingerprinting methods are used to detect IP piracy, then detection capability is improved, but the methods are prone to removal, masking, or forging attacks
Solution Approach 1:
The patent replaces traditional watermarking and fingerprinting mechanisms with a GNN-based automated detection system. Instead of embedding detectable signatures that can be attacked, the system uses graph neural networks to analyze circuit behavior and identify piracy through pattern recognition, substituting mechanical embedding methods with intelligent analysis
2Reliability
If conventional preventive countermeasures (logic encryption, camouflaging) are applied, then IP protection is improved, but implementation overhead increases
Solution Approach 1:
The patent extracts the security verification function from the hardware design itself and moves it to a separate automated GNN-based analysis system. Instead of embedding protection mechanisms within the IP that add overhead, the system extracts piracy detection to an external tool that analyzes design files without modifying the actual hardware implementation
3Measurement precision
If existing detection methods are used, then known threats can be identified, but scalability to large designs is limited
Solution Approach 1:
The patent implements a dynamic GNN-based detection system that can adapt to designs of varying sizes and complexities. The graph neural network automatically adjusts its analysis based on the input circuit structure, enabling scalable detection from small to large designs while maintaining accuracy through automated feature extraction and pattern recognition
4Measurement precision
If manual IP protection verification is performed, then detection thoroughness is improved, but time consumption increases
Solution Approach 1:
The patent implements a self-service automated detection system where the GNN-based tool independently analyzes design files, extracts features, and identifies potential piracy without requiring manual verification. The system performs thorough automated analysis of circuit graphs, eliminating time-consuming manual inspection while maintaining detection quality
Data Source
AI summary
The present invention is directed to an automated methodology based on GNN to model IC and mitigate hardware security threats. The present invention features a system for detecting hardware Trojans and IP piracy in a circuit. The system may be based on a GNN. The system may convert a plurality of base-level files from the circuit into a graph, convert this graph into a vectorized graph embedding through the use of the GNN, and use the vectorized graph embedding to identify and remedy potential hardware Trojans as well as identify potential IP piracy between two different circuits.