# Golden Reference-Free Hardware Trojan Localization using Graph Convolutional Network

arXiv: https://arxiv.org/abs/2207.06664 · HTML: https://mlsanity.com/paper/2207.06664/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 44 references that are in the map.
- First posted on arXiv 14 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations](https://mlsanity.com/paper/1911.07979/) (2019)
- [HW2VEC: A Graph Learning Tool for Automating Hardware Security](https://mlsanity.com/paper/2107.12328/) (2021)
- [GNN4IP: Graph Neural Network for Hardware Intellectual Property Piracy Detection](https://mlsanity.com/paper/2107.09130/) (2021)
- [Hardware Trojan Detection through Information Flow Security Verification](https://mlsanity.com/paper/1803.04102/) (2018)
- [Socio-network Analysis of RTL Designs for Hardware Trojan Localization](https://mlsanity.com/paper/1912.10312/) (2019)

## Built on by (most cited first, 2 of 2)

- [Graph Neural Networks: A Powerful and Versatile Tool for Advancing Design, Reliability, and Security of ICs](https://mlsanity.com/paper/2211.16495/) (2022)
- [TrojanWhisper: Evaluating Pre-trained LLMs to Detect and Localize Hardware Trojans](https://mlsanity.com/paper/2412.07636/) (2024)

## Abstract (arXiv)

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%.