Ren Runtao, Su Jinqi, Yang Ban, Lau Raymond Y K, Liu Qilei
School of Modern Post, Xi'an University of Posts and Telecommunications, Xi'an 710061, China.
School of Management and Economics, Xi'an University of Posts and Telecommunications, Xi'an 710061, China.
Entropy (Basel). 2022 Oct 28;24(11):1552. doi: 10.3390/e24111552.
Multilayer perceptron is composed of massive distributed neural processors interconnected. 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-Helleseth attack). This interdisciplinary work can attract more attention from academia and industry to the security of multilayer perceptron.
多层感知器由大量相互连接的分布式神经处理器组成。这些处理器中的非线性动态组件将输入数据扩展为突触的线性组合。然而，原始多层感知器在处理高复杂度信息时，其非线性映射能力有限。在多层感知器中引入更强大的非线性组件（如S盒），不仅可以增强其信息处理能力，还能提高整体安全性。因此，我们结合密码学和信息论方法，设计了一种在隐藏层采用熵编码的低功耗混沌S盒（LPC S盒），以使多层感知器更高效、安全地处理信息。在性能测试中，我们的S盒架构具有良好的特性，能够有效抵御主要的已知攻击（如Berlekamp Massey攻击和Ronjom-Helleseth攻击）。这项跨学科工作能够吸引学术界和工业界对多层感知器安全性更多的关注。
