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Background and Objectives: Substitution-box (s-box) is one of the essential components to create confusion and nonlinear properties in cryptography. To strengthening a cipher against various attacks, including side channel attacks, these boxes need to have numerous security properties. In this paper, a novel method to generate s-boxes is introduced aimed at improving the resistance of s-boxes against side channel attacks. Methods: In the preprocessing phase of this approach, a suitable initial s-box which has some basic security properties is generated by adopting a fast algorithm. Then, in the main stage, using the initial s-box, we generate new s-boxes which not only have the properties of the initial S-box but also have been significantly improved under another set of security properties. To do this, new s-boxes are generated using a genetic algorithm on a particular subset of the linear combination set of coordinate functions of the initial s-box in the preprocessing stage. Results: The performed experiments demonstrate that the values of all security properties of these new s-boxes, especially the measures of transparency order, signal-to-noise ratio, confusion coefficient, bijection property, fixed point, and opposite fixed points, have been substantially improved. For example, our experiments indicate that 70, 220, 2071, 43, and 406 s-boxes are found better than the initial s-box, respectively, in the dimensions of 4x4 through 8x8 Conclusion: In this article, a new s-box construction method is introduced in which the properties related to side channel attacks are improved, without reducing other security properties. Besides, some results obtained from generated s-boxes in the dimensions of 4x4 through 8x8 demonstrated that the generated s-boxes are not only improved relative to the initial s-box, but in some cases, considerably better than some well-known s-boxes.
The interplay between security and reliability is poorly understood. This paper shows how triple modular redundancy affects a side-channel attack (SCA). Our counterintuitive findings show that modular redundancy can increase SCA resiliency.
In recent years, various deep learning techniques have been exploited in side channel attacks, with the anticipation of obtaining more appreciable attack results. Most of them concentrate on improving network architectures or putting forward novel al
Numerous previous works have studied deep learning algorithms applied in the context of side-channel attacks, which demonstrated the ability to perform successful key recoveries. These studies show that modern cryptographic devices are increasingly t
Recently, many profiling side-channel attacks based on Machine Learning and Deep Learning have been proposed. Most of them focus on reducing the number of traces required for successful attacks by optimizing the modeling algorithms. In previous work,
Todays mobile devices contain densely packaged system-on-chips (SoCs) with multi-core, high-frequency CPUs and complex pipelines. In parallel, sophisticated SoC-assisted security mechanisms have become commonplace for protecting device data, such as