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A machine learning approach to predicting block cipher security

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dc.citedby4
dc.contributor.authorLee T.R.en_US
dc.contributor.authorTeh J.S.en_US
dc.contributor.authorYan J.L.S.en_US
dc.contributor.authorJamil N.en_US
dc.contributor.authorYeoh W.-Z.en_US
dc.contributor.authorid57219420025en_US
dc.contributor.authorid56579944200en_US
dc.contributor.authorid57219413724en_US
dc.contributor.authorid36682671900en_US
dc.contributor.authorid57205056525en_US
dc.date.accessioned2023-05-29T08:12:25Z
dc.date.available2023-05-29T08:12:25Z
dc.date.issued2020
dc.descriptionForecasting; Machine learning; Security of data; Turing machines; Block ciphers; Feistel structures; Hyperparameters; Machine learning approaches; Permutation patterns; Prediction accuracy; Security margins; Training data; Cryptographyen_US
dc.description.abstractExisting attempts in applying machine learning to cryptanalysis has seen limited success. This paper introduces an alternative approach in applying machine learning to block cipher cryptanalysis. Rather than trying to extract secret keys, machine learning classifiers are trained to predict a cipher's security margin with respect to the number of active s-boxes. Prediction is based on cipher features such as the number of rounds, permutation pattern, and truncated differences. Experiments are performed on a simplified generalised Feistel structure (GFS) block cipher. Prediction accuracy is optimised by refining how cipher features are represented as training data, and tuning hyperparameters. Results show that the machine learning classifiers are able formulate a relationship between the cipher features and security. When used to predict an unseen cipher (a cipher whose data was not used for training), an accuracy of up to 62% was obtained, depicting the feasibility of the proposed approach. � 2020 ACM.en_US
dc.description.natureFinalen_US
dc.identifier.epage132
dc.identifier.scopus2-s2.0-85092623519
dc.identifier.spage122
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85092623519&partnerID=40&md5=366c9a75e525541dccdaf7fff0f7681e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25664
dc.publisherInstitute for Mathematical Research (INSPEM)en_US
dc.sourceScopus
dc.sourcetitleProceedings of the 7th International Cryptology and Information Security Conference 2020, CRYPTOLOGY 2020
dc.titleA machine learning approach to predicting block cipher securityen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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