Hardware Trojan Insertion Using Reinforcement Learning

• URL: http://arxiv.org/abs/2204.04350v1
• Date: Sat, 9 Apr 2022 01:50:03 GMT
• Title: Hardware Trojan Insertion Using Reinforcement Learning
• Authors: Amin Sarihi, Ahmad Patooghy, Peter Jamieson, Abdel-Hameed A. Badawy
• Abstract summary: This paper utilizes Reinforcement Learning (RL) as a means to automate the Hardware Trojan (HT) insertion process. An RL agent explores the design space and finds circuit locations that are best for keeping inserted HTs hidden. Our toolset can insert combinational HTs into the ISCAS-85 benchmark suite with variations in HT size and triggering conditions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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