Trojan Playground: A Reinforcement Learning Framework for Hardware Trojan Insertion and Detection

TL;DR

This paper presents an innovative reinforcement learning framework for automated hardware Trojan insertion and detection, addressing biases and limitations of existing benchmarks to improve HT security analysis.

Contribution

It introduces the first RL-based framework for both inserting and detecting hardware Trojans, overcoming biases and one-dimensional analysis of prior benchmarks.

Findings

Effective HT insertion and detection demonstrated on ISCAS-85 benchmarks.

Achieved high success rates in hiding and discovering Trojans.

Provides a new methodology for benchmarking and comparing HT techniques.

Abstract

Current Hardware Trojan (HT) detection techniques are mostly developed based on a limited set of HT benchmarks. Existing HT benchmark circuits are generated with multiple shortcomings, i.e., i) they are heavily biased by the designers' mindset when created, and ii) they are created through a one-dimensional lens, mainly the signal activity of nets. We introduce the first automated Reinforcement Learning (RL) HT insertion and detection framework to address these shortcomings. In the HT insertion phase, an RL agent explores the circuits and finds locations best for keeping inserted HTs hidden. On the defense side, we introduce a multi-criteria RL-based HT detector that generates test vectors to discover the existence of HTs. Using the proposed framework, one can explore the HT insertion and detection design spaces to break the limitations of human mindset and benchmark issues, ultimately leading toward the next generation of innovative detectors. We demonstrate the efficacy of our framework on ISCAS-85 benchmarks, provide the attack and detection success rates, and define a methodology for comparing our techniques.