RELIC-GNN: Efficient State Registers Identification with Graph Neural Network for Reverse Engineering

TL;DR

RELIC-GNN employs a graph neural network to efficiently identify state registers in gate-level netlists, significantly improving accuracy and speed for reverse engineering tasks like Trojan detection and piracy prevention.

Contribution

This paper introduces RELIC-GNN, a novel GNN-based method for state register identification that outperforms previous techniques in efficiency and accuracy on large-scale netlists.

Findings

Achieves 100% recall in register identification

Improves precision by 30.49% over prior methods

Attains 88.37% overall accuracy

Abstract

Reverse engineering of gate-level netlist is critical for Hardware Trojans detection and Design Piracy counteracting. The primary task of gate-level reverse engineering is to separate the control and data signals from the netlist, which is mainly realized by identifying state registers with topological comparison.However, these methods become inefficient for large scale netlist. In this work, we propose RELIC-GNN, a graph neural network based state registers identification method, to address these issues. RELIC-GNN models the path structure of register as a graph and generates corresponding representation by considering node attributes and graph structure during training. The trained GNN model could be adopted to find the registers type very efficiently. Experimental results show that RELIC-GNN could achieve 100% in recall, 30.49% in precision and 88.37% in accuracy on average across different designs, which obtains significant improvements than previous approaches.