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AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning
We target five GNN-based techniques for four crucial classes of problems in hardware security: IP piracy, detecting/localizing HTs, reverse engineering, and hardware obfuscation.
Reinforcement Learning for Hardware Security: Opportunities, Developments, and Challenges
In this brief, we outline the development of RL agents in detecting hardware Trojans, one of the most challenging hardware security problems.
DETERRENT: Detecting Trojans using Reinforcement Learning
Insertion of hardware Trojans (HTs) in integrated circuits is a pernicious threat.
ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learning
Stealthy hardware Trojans (HTs) inserted during the fabrication of integrated circuits can bypass the security of critical infrastructures.
Attacking Split Manufacturing from a Deep Learning Perspective
The notion of integrated circuit split manufacturing which delegates the front-end-of-line (FEOL) and back-end-of-line (BEOL) parts to different foundries, is to prevent overproduction, piracy of the intellectual property (IP), or targeted insertion of hardware Trojans by adversaries in the FEOL facility.
Obfuscating the Interconnects: Low-Cost and Resilient Full-Chip Layout Camouflaging
Our scheme is based on obfuscating the interconnects (BEOL); it can be readily applied to any design without modifications in the device layer (FEOL).
Cryptography and Security
