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Surv."],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>Side-channel attacks represent a realistic and serious threat to the security of embedded devices for already almost three decades. A variety of attacks and targets they can be applied to have been introduced, and while the area of side-channel attacks and their mitigation is very well-researched, it is yet to be consolidated.<\/jats:p>\n          <jats:p>\n            Deep learning-based side-channel attacks entered the field in recent years with the promise of more competitive performance and enlarged attackers\u2019 capabilities compared to other techniques. At the same time, the new attacks bring new challenges and complexities to the domain, making the\n            <jats:bold>systematization of knowledge (SoK)<\/jats:bold>\n            even more critical.\n          <\/jats:p>\n          <jats:p>We first dissect deep learning-based side-channel attacks according to the different phases they can be used in and map those phases to the efforts conducted so far in the domain. For each phase, we identify the weaknesses and challenges that triggered the known open problems. We also connect the attacks to the threat models and evaluate their advantages and drawbacks. Finally, we provide a number of recommendations to be followed in deep learning-based side-channel attacks.<\/jats:p>","DOI":"10.1145\/3569577","type":"journal-article","created":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T11:48:29Z","timestamp":1666957709000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":145,"title":["SoK: Deep Learning-based Physical Side-channel Analysis"],"prefix":"10.1145","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7509-4337","authenticated-orcid":false,"given":"Stjepan","family":"Picek","sequence":"first","affiliation":[{"name":"Radboud University, Nijmegen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3799-7636","authenticated-orcid":false,"given":"Guilherme","family":"Perin","sequence":"additional","affiliation":[{"name":"Radboud University, Nijmegen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3089-6517","authenticated-orcid":false,"given":"Luca","family":"Mariot","sequence":"additional","affiliation":[{"name":"Radboud University, Nijmegen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7139-732X","authenticated-orcid":false,"given":"Lichao","family":"Wu","sequence":"additional","affiliation":[{"name":"Delft University of Technology, Delft, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0727-3573","authenticated-orcid":false,"given":"Lejla","family":"Batina","sequence":"additional","affiliation":[{"name":"Radboud University, Nijmegen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,2,9]]},"reference":[{"key":"e_1_3_4_2_2","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. 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(2022), 471.","journal-title":"IACR Cryptol. ePrint Arch."},{"key":"e_1_3_4_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2019.2926324"},{"key":"e_1_3_4_37_2","doi-asserted-by":"publisher","DOI":"10.5555\/3086952"},{"key":"e_1_3_4_38_2","first-page":"2672","volume-title":"Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8\u201313 2014, Montreal, Quebec, Canada","author":"Goodfellow Ian J.","year":"2014","unstructured":"Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8\u201313 2014, Montreal, Quebec, Canada, Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, and Kilian Q. Weinberger (Eds.). 2672\u20132680. https:\/\/proceedings.neurips.cc\/paper\/2014\/hash\/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html."},{"key":"e_1_3_4_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_4_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2017.7952132"},{"key":"e_1_3_4_41_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13389-019-00212-8"},{"key":"e_1_3_4_42_2","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1007\/978-3-030-38471-5_26","volume-title":"Selected Areas in Cryptography \u2013 SAC 2019","author":"Hettwer Benjamin","year":"2020","unstructured":"Benjamin Hettwer, Stefan Gehrer, and Tim G\u00fcneysu. 2020. Deep neural network attribution methods for leakage analysis and symmetric key recovery. In Selected Areas in Cryptography \u2013 SAC 2019, Kenneth G. Paterson and Douglas Stebila (Eds.). Springer International Publishing, Cham, 645\u2013666."},{"key":"e_1_3_4_43_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-29912-4_18"},{"key":"e_1_3_4_44_2","doi-asserted-by":"crossref","unstructured":"Johann Heyszl Andreas Ibing Stefan Mangard Fabrizio De Santis and Georg Sigl. 2013. Clustering Algorithms for Non-Profiled Single-Execution Attacks on Exponentiations.","DOI":"10.1007\/978-3-319-14123-7_6"},{"key":"e_1_3_4_45_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_4_46_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-45146-4_27"},{"key":"e_1_3_4_47_2","volume-title":"Advances in Neural Information Processing Systems","author":"Jaderberg Max","year":"2015","unstructured":"Max Jaderberg, Karen Simonyan, Andrew Zisserman, and koray kavukcuoglu. 2015. Spatial transformer networks. In Advances in Neural Information Processing Systems, C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett (Eds.), Vol. 28. Curran Associates, Inc.https:\/\/proceedings.neurips.cc\/paper\/2015\/file\/33ceb07bf4eeb3da587e268d663aba1a-Paper.pdf."},{"key":"e_1_3_4_48_2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/978-3-030-99766-3_2","volume-title":"Constructive Side-Channel Analysis and Secure Design","author":"Kerkhof Maikel","year":"2022","unstructured":"Maikel Kerkhof, Lichao Wu, Guilherme Perin, and Stjepan Picek. 2022. Focus is key to success: A focal loss function for deep learning-based side-channel analysis. In Constructive Side-Channel Analysis and Secure Design, Josep Balasch and Colin O\u2019Flynn (Eds.). Springer International Publishing, Cham, 29\u201348."},{"key":"e_1_3_4_49_2","doi-asserted-by":"publisher","DOI":"10.46586\/tches.v2019.i3.148-179"},{"key":"e_1_3_4_50_2","article-title":"State of the IoT 2020: 12 Billion IoT Connections, Surpassing Non-IoT for the First Time","author":"Lueth Knud Lasse","year":"2020","unstructured":"Knud Lasse Lueth. 2020. State of the IoT 2020: 12 Billion IoT Connections, Surpassing Non-IoT for the First Time. https:\/\/iot-analytics.com\/state-of-the-iot-2020-12-billion-iot-connections-surpassing-non-iot-for-the-first-time\/. Accessed August 4, 2021.","journal-title":"https:\/\/iot-analytics.com\/state-of-the-iot-2020-12-billion-iot-connections-surpassing-non-iot-for-the-first-time\/"},{"key":"e_1_3_4_51_2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-68697-5_9"},{"key":"e_1_3_4_52_2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-48405-1_25"},{"key":"e_1_3_4_53_2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-48405-1_25"},{"key":"e_1_3_4_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC42614.2022.9731739"},{"key":"e_1_3_4_55_2","first-page":"396","article-title":"Improving non-profiled side-channel attacks using autoencoder based preprocessing","volume":"2020","author":"Kwon Donggeun","year":"2020","unstructured":"Donggeun Kwon, HeeSeok Kim, and Seokhie Hong. 2020. Improving non-profiled side-channel attacks using autoencoder based preprocessing. IACR Cryptol. ePrint Arch. 2020 (2020), 396. https:\/\/eprint.iacr.org\/2020\/396.","journal-title":"IACR Cryptol. ePrint Arch."},{"key":"e_1_3_4_56_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13389-014-0089-3"},{"key":"e_1_3_4_57_2","doi-asserted-by":"publisher","DOI":"10.46586\/tches.v2021.i3.235-274"},{"key":"e_1_3_4_58_2","article-title":"Towards strengthening deep learning-based side channel attacks with mixup","volume":"2103","author":"Luo Zhimin","year":"2021","unstructured":"Zhimin Luo, Mengce Zheng, Ping Wang, Minhui Jin, Jiajia Zhang, Honggang Hu, and Nenghai Yu. 2021. Towards strengthening deep learning-based side channel attacks with mixup. CoRR abs\/2103.05833 (2021). arxiv:2103.05833. https:\/\/arxiv.org\/abs\/2103.05833.","journal-title":"CoRR"},{"key":"e_1_3_4_59_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-49445-6_1"},{"key":"e_1_3_4_60_2","volume-title":"Power Analysis Attacks: Revealing the Secrets of Smart Cards","author":"Mangard Stefan","year":"2006","unstructured":"Stefan Mangard, Elisabeth Oswald, and Thomas Popp. 2006. Power Analysis Attacks: Revealing the Secrets of Smart Cards. Springer. 338 pages. ISBN 0-387-30857-1, http:\/\/www.dpabook.org\/."},{"key":"e_1_3_4_61_2","volume-title":"Power Analysis Attacks: Revealing the Secrets of Smart Cards","author":"Mangard Stefan","year":"2008","unstructured":"Stefan Mangard, Elisabeth Oswald, and Thomas Popp. 2008. Power Analysis Attacks: Revealing the Secrets of Smart Cards. Vol. 31. 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