CRYPTOGRAPHY KEY GENERATION FOR ENHANCED DATA SECURITY USING WASSERSTEIN GENERATIVE ADVERSARIAL NETWORK (WGAN)

This paper investigates the challenges faced by cryptography due to the evolving nature of sophisticated cybercrime in this post quantum era. Looking at adverse consequences of quantum computing on cryptography most especially the sore strategies, this paper suggests the use of artificial intelligence in enhancing cryptography principles to prevent cybercrimes. This is because sophisticated attacks on digital devices caused by the advent of quantum computing, has render made the classical cryptography techniques outdated and susceptible to attack. For example, the capability of Shor’s strategy can rapidly detect discrete logarithms and integer factorization taking advantage of the RSA weakness whose encryption relies on the difficulty of factoring large prime factors and also, ECC algorithm whose key exchange relies in solving discrete logarithm problem. Other machine learning technique like Generative Adversarial Network (GAN) has been used to proffer solution but is faced with some challenges like poor convergence, training instability and mode collapse. This paper proposed the use of Wasserstein Generative Adversarial Network (WGAN) a machine learning technique for cryptographic key generation to enhanced the security of cryptography by generating dynamic keys in the presence of attack. The paper also discusses different types of cryptography techniques and analyse their potential challenges in this post-quantum era.