Troya Çagil Köylü, Cezar Rodolfo Wedig Reinbrecht, A. Gebregiorgis, S. Hamdioui, M. Taouil
{"title":"A Survey on Machine Learning in Hardware Security","authors":"Troya Çagil Köylü, Cezar Rodolfo Wedig Reinbrecht, A. Gebregiorgis, S. Hamdioui, M. Taouil","doi":"10.1145/3589506","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":50924,"journal":{"name":"ACM Journal on Emerging Technologies in Computing Systems","volume":"19 1","pages":"1 - 37"},"PeriodicalIF":2.1000,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Journal on Emerging Technologies in Computing Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3589506","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 1
Abstract
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.
期刊介绍：
The Journal of Emerging Technologies in Computing Systems invites submissions of original technical papers describing research and development in emerging technologies in computing systems. Major economic and technical challenges are expected to impede the continued scaling of semiconductor devices. This has resulted in the search for alternate mechanical, biological/biochemical, nanoscale electronic, asynchronous and quantum computing and sensor technologies. As the underlying nanotechnologies continue to evolve in the labs of chemists, physicists, and biologists, it has become imperative for computer scientists and engineers to translate the potential of the basic building blocks (analogous to the transistor) emerging from these labs into information systems. Their design will face multiple challenges ranging from the inherent (un)reliability due to the self-assembly nature of the fabrication processes for nanotechnologies, from the complexity due to the sheer volume of nanodevices that will have to be integrated for complex functionality, and from the need to integrate these new nanotechnologies with silicon devices in the same system.
The journal provides comprehensive coverage of innovative work in the specification, design analysis, simulation, verification, testing, and evaluation of computing systems constructed out of emerging technologies and advanced semiconductors
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