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TrojanForge: Adversarial Hardware Trojan Examples with Reinforcement Learning
The Hardware Trojan (HT) problem can be thought of as a continuous game between attackers and defenders, each striving to outsmart the other by leveraging any available means for an advantage.
Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation
We demonstrate the predictive power of our GNN model with a dataset consisting of 25, 000 samples from the noiseless IBM QASM Simulator and 12, 000 samples from three distinct noisy quantum backends.
The Seeker's Dilemma: Realistic Formulation and Benchmarking for Hardware Trojan Detection
The goal is to model HT detection more closely to the real world, i. e., describing the problem as "The Seeker's Dilemma" (an extension of Hide&Seek on a graph), where a detecting agent is unaware of whether circuits are infected by HTs or not.
Multi-criteria Hardware Trojan Detection: A Reinforcement Learning Approach
Hardware Trojans (HTs) are undesired design or manufacturing modifications that can severely alter the security and functionality of digital integrated circuits.
Hardware Trojan Insertion Using Reinforcement Learning
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.
