# DETERRENT: Detecting Trojans using Reinforcement Learning

arXiv: https://arxiv.org/abs/2208.12878 · HTML: https://mlsanity.com/paper/2208.12878/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 26 Aug 2022.

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

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Deep Reinforcement Learning for Cyber Security](https://mlsanity.com/paper/1906.05799/) (2019)
- [Robust Deep Reinforcement Learning for Security and Safety in Autonomous Vehicle Systems](https://mlsanity.com/paper/1805.00983/) (2018)
- [Deep Reinforcement Fuzzing](https://mlsanity.com/paper/1801.04589/) (2018)

## Built on by (most cited first, 5 of 5)

- [AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning](https://mlsanity.com/paper/2402.13946/) (2024)
- [Reinforcement Learning for Hardware Security: Opportunities, Developments, and Challenges](https://mlsanity.com/paper/2208.13885/) (2022)
- [Multi-criteria Hardware Trojan Detection: A Reinforcement Learning Approach](https://mlsanity.com/paper/2304.13232/) (2023)
- [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)

Insertion of hardware Trojans (HTs) in integrated circuits is a pernicious threat. Since HTs are activated under rare trigger conditions, detecting them using random logic simulations is infeasible. In this work, we design a reinforcement learning (RL) agent that circumvents the exponential search space and returns a minimal set of patterns that is most likely to detect HTs. Experimental results on a variety of benchmarks demonstrate the efficacy and scalability of our RL agent, which obtains a significant reduction (169×) in the number of test patterns required while maintaining or improving coverage (95.75%) compared to the state-of-the-art techniques.