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dc.contributor.authorBatina, Lejlaen_US
dc.contributor.authorBhasin, Shivamen_US
dc.contributor.authorJap, Dirmantoen_US
dc.contributor.authorPicek, Stjepanen_US
dc.identifier.citationBatina, L., Bhasin, S., Jap, D. & Picek, S. (2021). SCA strikes back : reverse engineering neural network architectures using side channels. IEEE Design and Test.
dc.description.abstractOur previous work selected for Top Picks in Hardware and Embedded Security 2020 demonstrates that it is possible to reverse engineer neural networks by using side-channel attacks. We developed a framework that considers each part of the neural network separately and then, by combining the information, manages to reverse engineer all relevant hyper-parameters and parameters. Our work is a proof of concept (but also a realistic demonstration) that such attacks are possible and warns that more effort should be given to developing countermeasures. While we have used microcontrollers for our experiments, the attack applies to other targets like FPGAs and GPUs.en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.relation.ispartofIEEE Design and Testen_US
dc.rights© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
dc.subjectScience::Mathematics::Discrete mathematics::Cryptographyen_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleSCA strikes back : reverse engineering neural network architectures using side channelsen_US
dc.typeJournal Articleen
dc.contributor.researchTemasek Laboratories @ NTUen_US
dc.description.versionAccepted versionen_US
dc.subject.keywordsBiological Neural Networksen_US
dc.description.acknowledgementThis research is partly supported by the National Research Foundation, Singapore, under its National Cybersecurity Research & Development Programme / Cyber-Hardware Forensic & Assurance Evaluation R&D Programme (Award: NRF2018NCR-NCR009-0001)en_US
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