# Node-wise Hardware Trojan Detection Based on Graph Learning

arXiv: https://arxiv.org/abs/2112.02213 · HTML: https://mlsanity.com/paper/2112.02213/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2021.

## Built on by (most cited first, 4 of 4)

- [TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs](https://mlsanity.com/paper/2506.17894/) (2025)
- [Automated Hardware Trojan Insertion in Industrial-Scale Designs](https://mlsanity.com/paper/2511.08703/) (2025)
- [Fast and Accurate Identification of Hardware Trojan Locations in Gate-Level Netlist using Nearest Neighbour Approach integrated with Machine Learning Technique](https://mlsanity.com/paper/2501.16347/) (2025)
- [Protocol effects on feature-based hardware-Trojan detection across Trust-Hub families](https://mlsanity.com/paper/2609.07199/) (2026)

## Abstract (arXiv)

In the fourth industrial revolution, securing the protection of the supply chain has become an ever-growing concern. One such cyber threat is a hardware Trojan (HT), a malicious modification to an IC. HTs are often identified in the hardware manufacturing process, but should be removed earlier, when the design is being specified. Machine learning-based HT detection in gate-level netlists is an efficient approach to identify HTs at the early stage. However, feature-based modeling has limitations in discovering an appropriate set of HT features. We thus propose NHTD-GL in this paper, a novel node-wise HT detection method based on graph learning (GL). Given the formal analysis of HT features obtained from domain knowledge, NHTD-GL bridges the gap between graph representation learning and feature-based HT detection. The experimental results demonstrate that NHTD-GL achieves 0.998 detection accuracy and outperforms state-of-the-art node-wise HT detection methods. NHTD-GL extracts HT features without heuristic feature engineering.