# Multi-criteria Hardware Trojan Detection: A Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2304.13232 · HTML: https://mlsanity.com/paper/2304.13232/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 26 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [HW2VEC: A Graph Learning Tool for Automating Hardware Security](https://mlsanity.com/paper/2107.12328/) (2021)
- [Hardware Trojan Insertion Using Reinforcement Learning](https://mlsanity.com/paper/2204.04350/) (2022)
- [DETERRENT: Detecting Trojans using Reinforcement Learning](https://mlsanity.com/paper/2208.12878/) (2022)

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

- [The Seeker's Dilemma: Realistic Formulation and Benchmarking for Hardware Trojan Detection](https://mlsanity.com/paper/2402.17918/) (2024)
- [TrojanForge: Generating Adversarial Hardware Trojan Examples Using Reinforcement Learning](https://mlsanity.com/paper/2405.15184/) (2024)

## Abstract (arXiv)

Hardware Trojans (HTs) are undesired design or manufacturing modifications that can severely alter the security and functionality of digital integrated circuits. HTs can be inserted according to various design criteria, e.g., nets switching activity, observability, controllability, etc. However, to our knowledge, most HT detection methods are only based on a single criterion, i.e., nets switching activity. This paper proposes a multi-criteria reinforcement learning (RL) HT detection tool that features a tunable reward function for different HT detection scenarios. The tool allows for exploring existing detection strategies and can adapt new detection scenarios with minimal effort. We also propose a generic methodology for comparing HT detection methods fairly. Our preliminary results show an average of 84.2% successful HT detection in ISCAS-85 benchmark