ReIGNN: State Register Identification Using Graph Neural Networks for Circuit Reverse Engineering

TL;DR

ReIGNN introduces a graph neural network-based method combined with structural analysis to accurately classify registers in circuit netlists, aiding reverse engineering and security analysis.

Contribution

The paper presents a novel GNN-based approach with structural analysis for high-accuracy register classification in circuit reverse engineering.

Findings

Achieves 96.5% balanced accuracy on benchmarks

Attains 97.7% sensitivity across designs

Effective in generalizing to different circuit designs

Abstract

Reverse engineering an integrated circuit netlist is a powerful tool to help detect malicious logic and counteract design piracy. A critical challenge in this domain is the correct classification of data-path and control-logic registers in a design. We present ReIGNN, a novel learning-based register classification methodology that combines graph neural networks (GNNs) with structural analysis to classify the registers in a circuit with high accuracy and generalize well across different designs. GNNs are particularly effective in processing circuit netlists in terms of graphs and leveraging properties of the nodes and their neighborhoods to learn to efficiently discriminate between different types of nodes. Structural analysis can further rectify any registers misclassified as state registers by the GNN by analyzing strongly connected components in the netlist graph. Numerical results on a set of benchmarks show that ReIGNN can achieve, on average, 96.5% balanced accuracy and 97.7% sensitivity across different designs.