KATAN ciphers are block ciphers using non‐linear feedback shift registers. In this study, the authors improve the results of conditional differential analysis on KATAN by using deep learning. Multi‐differential neural distinguishers are built to improve the accuracy of the neural distinguishers and increase the number of its rounds. Moreover, a conditional differential analysis framework is proposed based on deep learning with the multi‐differential neural distinguishers, resulting in a significant improvement than the previous. We present a practical key recovery attack on the 97‐round KATAN32 with 215.5 data complexity and 220.5 time complexity. The attack of the 82‐round KATAN48 and 70‐round KATAN64 are also presented as the best known practical results.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.