Beyond Random Inputs: A Novel ML-Based Hardware Fuzzing

arXiv:2404.06856v1 Announce Type: cross
Abstract: Modern computing systems heavily rely on hardware as the root of trust. However, their increasing complexity has given rise to security-critical vulnerabilities that cross-layer at-tacks can exploit. Traditional hardware vulnerability detection methods, such as random regression and formal verification, have limitations. Random regression, while scalable, is slow in exploring hardware, and formal verification techniques are often concerned with manual effort and state explosions. Hardware fuzzing has emerged as an effective approach to exploring and detecting …

abstract arxiv beyond complexity computing computing systems cs.ar cs.cr cs.lg cs.se detection detection methods exploit fuzzing hardware however inputs layer limitations modern novel random regression scalable security systems trust type verification vulnerabilities vulnerability