TY - JOUR
T1 - A Survey on Machine Learning in Hardware Security
AU - Köylü, T.C.
AU - Reinbrecht, Cezar
AU - Gebregiorgis, A.B.
AU - Hamdioui, S.
AU - Taouil, M.
PY - 2023
Y1 - 2023
N2 - Hardware security is currently a very influential domain, where each year countless works are published concerning attacks against hardware and countermeasures. A significant number of them use machine learning, which is proven to be very effective in other domains. This survey, as one of the early attempts, presents the usage of machine learning in hardware security in a full and organized manner. Our contributions include classification and introduction to the relevant fields of machine learning, a comprehensive and critical overview of machine learning usage in hardware security, and an investigation of the hardware attacks against machine learning (neural network) implementations.
AB - Hardware security is currently a very influential domain, where each year countless works are published concerning attacks against hardware and countermeasures. A significant number of them use machine learning, which is proven to be very effective in other domains. This survey, as one of the early attempts, presents the usage of machine learning in hardware security in a full and organized manner. Our contributions include classification and introduction to the relevant fields of machine learning, a comprehensive and critical overview of machine learning usage in hardware security, and an investigation of the hardware attacks against machine learning (neural network) implementations.
UR - http://www.scopus.com/inward/record.url?scp=85162033755&partnerID=8YFLogxK
U2 - 10.1145/3589506
DO - 10.1145/3589506
M3 - Article
SN - 1550-4832
VL - 19
JO - ACM Journal on Emerging Technologies in Computing Systems
JF - ACM Journal on Emerging Technologies in Computing Systems
IS - 2
M1 - 18
ER -