# HW2VEC: A Graph Learning Tool for Automating Hardware Security

arXiv: https://arxiv.org/abs/2107.12328 · HTML: https://mlsanity.com/paper/2107.12328/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 53 references that are in the map.
- First posted on arXiv 26 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [Self-Attention Graph Pooling](https://mlsanity.com/paper/1904.08082/) (2019)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [Understanding Attention and Generalization in Graph Neural Networks](https://mlsanity.com/paper/1905.02850/) (2019)
- [Computing Graph Neural Networks: A Survey from Algorithms to Accelerators](https://mlsanity.com/paper/2010.00130/) (2020)
- [Embedding Projector: Interactive Visualization and Interpretation of Embeddings](https://mlsanity.com/paper/1611.05469/) (2016)
- [GNN4IP: Graph Neural Network for Hardware Intellectual Property Piracy Detection](https://mlsanity.com/paper/2107.09130/) (2021)
- [Raise Your Game for Split Manufacturing: Restoring the True Functionality Through BEOL](https://mlsanity.com/paper/1806.09135/) (2018)

## Built on by (most cited first, 16 of 16)

- [LLMPirate: LLMs for Black-box Hardware IP Piracy](https://mlsanity.com/paper/2411.16111/) (2024)
- [AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning](https://mlsanity.com/paper/2402.13946/) (2024)
- [Graph Neural Networks: A Powerful and Versatile Tool for Advancing Design, Reliability, and Security of ICs](https://mlsanity.com/paper/2211.16495/) (2022)
- [Golden Reference-Free Hardware Trojan Localization using Graph Convolutional Network](https://mlsanity.com/paper/2207.06664/) (2022)
- [Multi-criteria Hardware Trojan Detection: A Reinforcement Learning Approach](https://mlsanity.com/paper/2304.13232/) (2023)
- [BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulation](https://mlsanity.com/paper/2401.10753/) (2024)
- [TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion](https://mlsanity.com/paper/2601.17178/) (2026)
- [A Survey and Perspective on Artificial Intelligence for Security-Aware Electronic Design Automation](https://mlsanity.com/paper/2204.09579/) (2022)
- [Uncertainty-Aware Hardware Trojan Detection Using Multimodal Deep Learning](https://mlsanity.com/paper/2401.09479/) (2024)
- [The Seeker's Dilemma: Realistic Formulation and Benchmarking for Hardware Trojan Detection](https://mlsanity.com/paper/2402.17918/) (2024)
- [A Benchmark on Directed Graph Representation Learning in Hardware Designs](https://mlsanity.com/paper/2410.06460/) (2024)
- [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)
- [STELLAR: Structure-guided LLM Assertion Retrieval and Generation for Formal Verification](https://mlsanity.com/paper/2601.19903/) (2025)
- [Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL](https://mlsanity.com/paper/2603.09161/) (2026)
- [ADVERSARIAL: And-Inverter Graph-Assisted Hardware Trojan Detection At Scale](https://mlsanity.com/paper/2607.23882/) (2026)

## Abstract (arXiv)

The time-to-market pressure and continuous growing complexity of hardware designs have promoted the globalization of the Integrated Circuit (IC) supply chain. However, such globalization also poses various security threats in each phase of the IC supply chain. Although the advancements of Machine Learning (ML) have pushed the frontier of hardware security, most conventional ML-based methods can only achieve the desired performance by manually finding a robust feature representation for circuits that are non-Euclidean data. As a result, modeling these circuits using graph learning to improve design flows has attracted research attention in the Electronic Design Automation (EDA) field. However, due to the lack of supporting tools, only a few existing works apply graph learning to resolve hardware security issues. To attract more attention, we propose HW2VEC, an open-source graph learning tool that lowers the threshold for newcomers to research hardware security applications with graphs. HW2VEC provides an automated pipeline for extracting a graph representation from a hardware design in various abstraction levels (register transfer level or gate-level netlist). Besides, HW2VEC users can automatically transform the non-Euclidean hardware designs into Euclidean graph embeddings for solving their problems. In this paper, we demonstrate that HW2VEC can achieve state-of-the-art performance on two hardware security-related tasks: Hardware Trojan Detection and Intellectual Property Piracy Detection. We provide the time profiling results for the graph extraction and the learning pipelines in HW2VEC.