Particle Swarm Optimization Based Highly Nonlinear Substitution-Boxes Generation for Security Applications

Abstract

Symmetric encryption has been considered as one of the essential means of ensuring security of end to end communication. The robustness and strength of modern day block encryption systems are based on the cryptographic features of substitution-boxes which are used to inject confusion ability during substitution-phases. In this paper, as an alternative to random, chaos or algebraic based construction methods, we propose to present an efficient method for the generation of cryptographic highly nonlinear substitution-boxes. The nature-inspired particle swarm optimization is reconnoitered to develop proposed method wherein the initial population is generated through a simple but with rich dynamics chaotic Renyi map. The anticipated method is analyzed for different scenarios such as change in population size, number of iterations, and linear increase in inertial weight. The performance assessment of generated S-boxes under standard criterions corroborate that the proposed method has excellent cryptographic features and found grander than many recent optimization based S-box methods. Moreover, an image encryption application of proposed S-boxes is also suggested to determine their suitability and applicability for image based security applications.

Evolving Nonlinear S-Boxes With Improved Theoretical Resilience to Power Attacks

“…In 2020, Wang et al introduced a novel GA for the generation of bijective substitution boxes with high nonlinearity [49]. Besides the aforementioned, there are some recently published papers that combine chaotic number generators, elliptic curves, quantum computing, and heuristic methods in the design of dynamic and key-dependent S-boxes with good cryptographic properties [22]- [27], [50]- [56], [58]- [60].…”

A Method to Determine the Most Suitable Initial Conditions of Chaotic Map in Statistical Randomness Applications

“…[43][44][45], the design criteria have been reached. It has also been shown in different studies that a similar approach can be achieved by optimizing the results [46,47]. Although there is a possibility to use these design approaches to improve s-box criteria; the performance of sbox structures to be generated from random bit sequences have been analyzed.…”

Human Behaviour Based Optimization Supported With Self-Organizing Maps for Solving the S-Box Design Problem

“…random methods [5], algebraic constructions, chaotic maps and heuristic methods. As examples of the last method, we can mention works carried out using Ant Colony Optimization [6], Genetic Algorithm [7], [8], Simulated Annealing [9], [10], Leaders and Followers [11] and Particle Swarm Optimization [12]. In this paper, we use a novel optimization algorithm based on the human behavior [13] integrated it with Self-Organizing Maps in order to avoid the problem of premature convergence [14].…”