Hardware Trojan Insertion Using Reinforcement Learning

TL;DR

This paper presents a reinforcement learning-based method to automate hardware Trojan insertion, aiming to improve stealthiness and robustness of Trojans by exploring optimal circuit locations without human bias.

Contribution

It introduces a novel RL framework for automated HT insertion, capable of generating highly concealed Trojans with minimal footprint and high input coverage.

Findings

Achieves 100% input coverage in benchmark circuits

Inserts Trojans with minimal footprint and rare activation

Demonstrates effectiveness of RL approach in stealthy HT insertion

Abstract

This paper utilizes Reinforcement Learning (RL) as a means to automate the Hardware Trojan (HT) insertion process to eliminate the inherent human biases that limit the development of robust HT detection methods. An RL agent explores the design space and finds circuit locations that are best for keeping inserted HTs hidden. To achieve this, a digital circuit is converted to an environment in which an RL agent inserts HTs such that the cumulative reward is maximized. Our toolset can insert combinational HTs into the ISCAS-85 benchmark suite with variations in HT size and triggering conditions. Experimental results show that the toolset achieves high input coverage rates (100\% in two benchmark circuits) that confirms its effectiveness. Also, the inserted HTs have shown a minimal footprint and rare activation probability.