An improved integral distinguisher scheme based on neural networks

International Journal of Intelligent SystemsVolume 37, Issue 10 p. 7584-7613 RESEARCH ARTICLE An improved integral distinguisher scheme based on neural networks Behnam Zahednejad, Corresponding Author Behnam Zahednejad [email protected] orcid.org/0000-0001-6802-1084 Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China Correspondence Behnam Zahednejad, Institute of Artificial Intelligence and Blockchain, Guangzhou University, 230 Wai Huan Xi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, P.R. China. Email: [email protected]Search for more papers by this authorLijun Lyu, Lijun Lyu State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, China University of Chinese Academy of Sciences, Beijing, ChinaSearch for more papers by this author Behnam Zahednejad, Corresponding Author Behnam Zahednejad [email protected] orcid.org/0000-0001-6802-1084 Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China Correspondence Behnam Zahednejad, Institute of Artificial Intelligence and Blockchain, Guangzhou University, 230 Wai Huan Xi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, P.R. China. Email: [email protected]Search for more papers by this authorLijun Lyu, Lijun Lyu State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, China University of Chinese Academy of Sciences, Beijing, ChinaSearch for more papers by this author First published: 09 May 2022 https://doi.org/10.1002/int.22895Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract At CRYPTO 2019, A. Gohr made a breakthrough in combining classical cryptanalysis and deep learning and applied his method to round reduced SPECK successfully. However, his suggested neural-based distinguisher scheme is only limited to differential cryptanalysis. In this paper, we have the following contributions: 1. We combine integral cryptanalysis and deep learning to propose our neural-based integral distinguisher scheme for the first time. To illustrate the effectiveness of our distinguisher scheme, we apply it to block ciphers of different structures, such as substitution-permutation structure ciphers (PRESENT and RECTANGLE), Feistel structure cipher (LBLOCK), and add-rotate-XOR cipher (SPECK) and compare the results with the state-of-the-art classical integral distinguishing method, namely, the bit-based division property. To our great surprise, our neural network-based integral distinguisher can extend the number of distinguished rounds for all block ciphers by two additional rounds (except RECTANGLE, where it is improved by one round) under the same data complexity. 2. As an additional advantage of our scheme, we demonstrate that our Neural Distinguisher (ND) is not only helpful for block cipher designers but also can assist attackers to mount key recovery attacks. To this end, we show how to exploit our ND to mount a key recovery attack and apply it to SPECK32/64. Out of the 1000 trials of key recovery attacks with different keys in 45% of cases, the first suggested subkey is exactly the real subkey of the last round of the cipher. For the remaining 55%, the second or third suggested subkey is exactly the real subkey of the last round of the cipher. 3. To have a piece of concrete evidence for the advantage of our scheme over classical integral methods, we design an experiment known as the same-difference experiment. In this experiment, we show that our ND can learn some features beyond the capabilities of classical integral methods. 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