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The nonlinear dynamic components in these processors expand the input data into a linear combination of synapses. However, the nonlinear mapping ability of original multilayer perceptron is limited when processing high complexity information. The introduction of more powerful nonlinear components (e.g., S-box) to multilayer perceptron can not only reinforce its information processing ability, but also enhance the overall security. Therefore, we combine the methods of cryptography and information theory to design a low-power chaotic S-box (LPC S-box) with entropy coding in the hidden layer to make the multilayer perceptron process information more efficiently and safely. In the performance test, our S-box architecture has good properties, which can effectively resist main known attacks (e.g., Berlekamp Massey-attack and Ronjom\u2013Helleseth attack). This interdisciplinary work can attract more attention from academia and industry to the security of multilayer perceptron.<\/jats:p>","DOI":"10.3390\/e24111552","type":"journal-article","created":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T04:57:34Z","timestamp":1667105854000},"page":"1552","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Novel Low-Power Construction of Chaotic S-Box in Multilayer Perceptron"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6480-8816","authenticated-orcid":false,"given":"Runtao","family":"Ren","sequence":"first","affiliation":[{"name":"School of Modern Post, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710061, China"},{"name":"School of Management and Economics, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710061, China"},{"name":"Department of Information Systems, City University of Hong Kong, Kowloon Tong, Hong Kong, China"}]},{"given":"Jinqi","family":"Su","sequence":"additional","affiliation":[{"name":"School of Management and Economics, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710061, China"}]},{"given":"Ban","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}]},{"given":"Raymond Y. K.","family":"Lau","sequence":"additional","affiliation":[{"name":"Department of Information Systems, City University of Hong Kong, Kowloon Tong, Hong Kong, China"}]},{"given":"Qilei","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Management and Economics, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710061, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e12683","DOI":"10.1111\/jfr3.12683","article-title":"Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks","volume":"14","author":"Ahmadlou","year":"2020","journal-title":"J. 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