{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:25:42Z","timestamp":1750220742881,"version":"3.41.0"},"reference-count":38,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T00:00:00Z","timestamp":1600732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"DeitY, Government of India (Information Security Education Awareness","award":["IIT\/SRIC\/CSE\/IIP\/2015-16\/107"],"award-info":[{"award-number":["IIT\/SRIC\/CSE\/IIP\/2015-16\/107"]}]},{"name":"Singapore National Research Foundation \u201cSOCure\u201d","award":["NRF2018NCRNCR002-0001"],"award-info":[{"award-number":["NRF2018NCRNCR002-0001"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Emerg. Technol. Comput. Syst."],"published-print":{"date-parts":[[2021,1,31]]},"abstract":"<jats:p>\n            Fault injection-based cryptanalysis is one of the most powerful practical threats to modern cryptographic primitives. Popular countermeasures to such fault-based attacks generally use some form of redundant computation to detect and react\/correct the injected faults. However, such countermeasures are shown to be vulnerable to selective fault injections. In this article, we aim to develop a cryptographic primitive that is\n            <jats:italic>fault tolerant by its construction<\/jats:italic>\n            and does not require to compute the same value multiple times. We utilize the effectiveness of Neural Networks (NNs), which show \u201csome degree\u201d of robustness by functioning correctly even after the occurrence of faults in any of its parameters. We also propose a novel strategy that enhances the fault tolerance of the implementation to \u201chigh degree\u201d (close to 100%) by incorporating selective constraints in the NN parameters during the training phase. We evaluated the performance of revised NN considering both software and FPGA implementations for standard cryptographic primitives like 8\u00d78 AES SBox and 4\u00d74 PRESENT SBox. The results show that the fault tolerance of such implementations can be significantly increased with the proposed methodology. Such NN-based cryptographic primitives will provide inherent resistance against fault injections without requiring any redundancy countermeasures.\n          <\/jats:p>","DOI":"10.1145\/3409594","type":"journal-article","created":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T10:33:44Z","timestamp":1600770824000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Neural Network-based Inherently Fault-tolerant Hardware Cryptographic Primitives without Explicit Redundancy Checks"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3338-2944","authenticated-orcid":false,"given":"Manaar","family":"Alam","sequence":"first","affiliation":[{"name":"Indian Institute of Technology Kharagpur, Kharagpur, India"}]},{"given":"Arnab","family":"Bag","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Kharagpur, Kharagpur, India"}]},{"given":"Debapriya Basu","family":"Roy","sequence":"additional","affiliation":[{"name":"Technical University of Munich, Munich, Germany"}]},{"given":"Dirmanto","family":"Jap","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}]},{"given":"Jakub","family":"Breier","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}]},{"given":"Shivam","family":"Bhasin","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}]},{"given":"Debdeep","family":"Mukhopadhyay","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Kharagpur, Kharagpur, India"}]}],"member":"320","published-online":{"date-parts":[[2020,9,22]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-21969-6_21"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/DFTVS.2002.1173501"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2003.1190590"},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the 9th International Workshop Cryptographic Hardware and Embedded Systems (CHES\u201907)","volume":"4727","author":"Bogdanov Andrey","unstructured":"Andrey Bogdanov , Lars R. Knudsen , Gregor Leander , Christof Paar , Axel Poschmann , Matthew J. B. Robshaw , Yannick Seurin , and C. Vikkelsoe . 2007. PRESENT: An ultra-lightweight block cipher . In Proceedings of the 9th International Workshop Cryptographic Hardware and Embedded Systems (CHES\u201907) , Pascal Paillier and Ingrid Verbauwhede (Eds.), Lecture Notes in Computer Science , Vol. 4727 . Springer, 450--466. DOI:https:\/\/doi.org\/10.1007\/978-3-540-74735-2_31 10.1007\/978-3-540-74735-2_31 Andrey Bogdanov, Lars R. Knudsen, Gregor Leander, Christof Paar, Axel Poschmann, Matthew J. B. Robshaw, Yannick Seurin, and C. Vikkelsoe. 2007. PRESENT: An ultra-lightweight block cipher. In Proceedings of the 9th International Workshop Cryptographic Hardware and Embedded Systems (CHES\u201907), Pascal Paillier and Ingrid Verbauwhede (Eds.), Lecture Notes in Computer Science, Vol. 4727. Springer, 450--466. DOI:https:\/\/doi.org\/10.1007\/978-3-540-74735-2_31"},{"key":"e_1_2_1_5_1","unstructured":"Fran\u00e7ois Chollet et\u00a0al. 2015. Keras. Retrieved from https:\/\/keras.io.  Fran\u00e7ois Chollet et\u00a0al. 2015. Keras. Retrieved from https:\/\/keras.io."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00089"},{"volume-title":"The Design of Rijndael: AES\u2014The Advanced Encryption Standard","author":"Daemen Joan","key":"e_1_2_1_7_1","unstructured":"Joan Daemen and Vincent Rijmen . 2002. The Design of Rijndael: AES\u2014The Advanced Encryption Standard . Springer . DOI:https:\/\/doi.org\/10.1007\/978-3-662-04722-4 10.1007\/978-3-662-04722-4 Joan Daemen and Vincent Rijmen. 2002. The Design of Rijndael: AES\u2014The Advanced Encryption Standard. Springer. 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Xiaolu Hou, Jakub Breier, Dirmanto Jap, Lei Ma, Shivam Bhasin, and Yang Liu. 2019. Experimental evaluation of deep neural network resistance against fault injection attacks. IACR Cryptol. ePrint Arch. 2019 (2019), 461. https:\/\/eprint.iacr.org\/2019\/461.","journal-title":"IACR Cryptol. ePrint Arch."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-45238-6_10"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2010.33"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/11495772_38"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAD.2017.8203770"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2008.149"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/11889700_15"},{"volume-title":"Fault tolerance of the backpropagation neural network trained on noisy inputs. 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