Golden Reference-Free Hardware Trojan Localization using Graph Convolutional Network

TL;DR

This paper introduces a novel graph convolutional network-based method for localizing hardware Trojans in integrated circuits without needing a golden reference, achieving high accuracy and low false positives at the pre-silicon stage.

Contribution

It presents the first reference-free GCN approach for hardware Trojan localization, overcoming limitations of prior methods such as scalability and manual feature engineering.

Findings

Achieves 99.6% localization accuracy

F1-score of 93.1% in Trojan detection

False-positive rate below 0.009%

Abstract

The globalization of the Integrated Circuit (IC) supply chain has moved most of the design, fabrication, and testing process from a single trusted entity to various untrusted third-party entities worldwide. The risk of using untrusted third-Party Intellectual Property (3PIP) is the possibility for adversaries to insert malicious modifications known as Hardware Trojans (HTs). These HTs can compromise the integrity, deteriorate the performance, deny the service, and alter the functionality of the design. While numerous HT detection methods have been proposed in the literature, the crucial task of HT localization is overlooked. Moreover, a few existing HT localization methods have several weaknesses: reliance on a golden reference, inability to generalize for all types of HT, lack of scalability, low localization resolution, and manual feature engineering/property definition. To overcome their shortcomings, we propose a novel, golden reference-free HT localization method at the pre-silicon stage by leveraging Graph Convolutional Network (GCN). In this work, we convert the circuit design to its intrinsic data structure, graph and extract the node attributes. Afterward, the graph convolution performs automatic feature extraction for nodes to classify the nodes as Trojan or benign. Our automated approach does not burden the designer with manual code review. It locates the Trojan signals with 99.6% accuracy, 93.1% F1-score, and a false-positive rate below 0.009%.