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Title: SCA strikes back : reverse engineering neural network architectures using side channels
Authors: Batina, Lejla
Bhasin, Shivam
Jap, Dirmanto
Picek, Stjepan
Keywords: Science::Mathematics::Discrete mathematics::Cryptography
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2021
Source: Batina, L., Bhasin, S., Jap, D. & Picek, S. (2021). SCA strikes back : reverse engineering neural network architectures using side channels. IEEE Design and Test.
Project: NRF2018NCR-NCR009-0001 
Journal: IEEE Design and Test 
Abstract: Our 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.
ISSN: 2168-2364
DOI: 10.1109/MDAT.2021.3128436
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:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:TL Journal Articles

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