Publication:
On the security of lightweight block ciphers against neural distinguishers: Observations on LBC-IoT and SLIM

Simple item page
dc.citedby3
dc.contributor.authorTeng W.J.en_US
dc.contributor.authorTeh J.S.en_US
dc.contributor.authorJamil N.en_US
dc.contributor.authorid57193064876en_US
dc.contributor.authorid56579944200en_US
dc.contributor.authorid36682671900en_US
dc.date.accessioned2024-10-14T03:17:56Z
dc.date.available2024-10-14T03:17:56Z
dc.date.issued2023
dc.description.abstractInterest in the application of deep learning in cryptography has increased immensely in recent years. Several works have shown that such attacks are not only feasible but, in some cases, are superior compared to classical cryptanalysis techniques. However, due to the black-box nature of deep learning models, more work is required to understand how they work in the context of cryptanalysis. In this paper, we contribute towards the latter by first constructing neural distinguishers for 2 different block ciphers, LBC-IoT and SLIM that share similar properties. We then show that, unlike classical differential cryptanalysis (on which neural distinguishers are based), the position where the round keys are included in round functions can have a significant impact on distinguishing probability. We explore this further to investigate if different choices of where the round key is introduced can lead to better resistance against neural distinguishers. We compare several variants of the round function to showcase this phenomenon, which is useful for securing future block cipher designs against deep learning attacks. As an additional contribution, the neural distinguisher for LBC-IoT was also applied in a practical-time key recovery attack on up to 8 rounds. Results show that even with no optimizations, the attack can consistently recover the correct round key with an attack complexity of around 224 full encryptions. To the best of our knowledge, this is the first third-party cryptanalysis results for LBC-IoT to date. � 2023 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo103531
dc.identifier.doi10.1016/j.jisa.2023.103531
dc.identifier.scopus2-s2.0-85163701461
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85163701461&doi=10.1016%2fj.jisa.2023.103531&partnerID=40&md5=9c7b0991af00647dd4771b7744a78c55
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34094
dc.identifier.volume76
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleJournal of Information Security and Applications
dc.subjectBlock cipher
dc.subjectDeep learning
dc.subjectDifferential cryptanalysis
dc.subjectLightweight cryptography
dc.subjectNeural distinguisher
dc.subjectNeural network
dc.subjectDeep learning
dc.subjectInternet of things
dc.subjectLyapunov methods
dc.subjectSecurity of data
dc.subjectBlock ciphers
dc.subjectDeep learning
dc.subjectDifferential cryptanalysis
dc.subjectDistinguishers
dc.subjectLight-weight cryptography
dc.subjectLightweight block ciphers
dc.subjectNeural distinguisher
dc.subjectNeural-networks
dc.subjectRound functions
dc.subjectRound key
dc.subjectCryptography
dc.titleOn the security of lightweight block ciphers against neural distinguishers: Observations on LBC-IoT and SLIMen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS