Prospects of Cryptanalysis using Generative AI

Abstract

The rapid evolution of artificial intelligence (AI) technologies, particularly generative AI, has introduced transformative possibilities across various domains, including cryptography and cryptanalysis. This research paper explores the prospects of employing generative AI techniques to enhance cryptanalysis, a field traditionally reliant on mathematical and heuristic methods. I have provided a comprehensive literature review that outlines the current state of cryptographic practices and identifies significant research gaps in the application of AI. My proposed methodology involves the use of generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), to analyze encrypted data and uncover vulnerabilities in cryptographic algorithms. Through a series of experiments, I evaluated the performance of these models in breaking various encryption schemes, including AES, RSA, and DES. The results demonstrate that generative AI can achieve high accuracy rates in decrypting messages, revealing both the potential benefits and risks associated with its application in cryptanalysis. This paper concludes with a discussion on the implications for future cryptographic practices, ethical considerations, and directions for further research, emphasizing the need for adaptive security measures in an increasingly AI-driven landscape.

Keywords: Cryptanalysis, Generative AI, Machine Learning, Cryptography, Security, Neural Networks, Data Science, Encryption, Decryption, AI Ethics