Dynamic S-boxes generation for IoT security enhancement: A genetic algorithm approach

Abstract

We are witnessing an explosion in the concept of the Internet of Things (IoT) as smart objects become more important in our daily lives to make it easier to achieve our goals. It is obvious that the application areas of IoT are extremely diverse. Indeed, several sectors will be deeply impacted by IoT applications. Of which, security in the Internet of Things (IoT) has become a major concern due to increasing vulnerabilities and threats. Connected devices, often with limited security capabilities, have become attractive targets for attackers. Vulnerabilities in IoT devices, whether resulting from unmodified default configurations, lack of regular updates, or insecure communication protocols, expose these systems to potential risks. Cryptography, as a discipline dedicated to securing communications, uses advanced mathematical techniques to guarantee the confidentiality of electronic exchanges by making the data incomprehensible to unauthorized persons.

In this paper, we show an in-depth exploration of the generation of new S-boxes using the genetic algorithm. These new S-boxes are dynamically generated with the new genetic algorithm-based approach. This new method offers adaptive optimization, iteratively exploring the solution space to identify robust S-boxes, thereby strengthening the security of the encryption system. The study continues with the evaluation of these S-boxes through dedicated metrics, allowing an in-depth analysis of their performance in terms of resistance to cryptographic attacks and preservation of substitution properties. Finally, this evaluation extends to the incorporation of these newly generated S-boxes into the AES encryption algorithm, where different dimensions, such as cryptanalysis resistance, scene visibility, and other performance levels, will be examined.