Artificial Neural Networks in Cryptography: Applications, Challenges, and Future Directions for Secure Systems

Abstract

Cryptography is a critical pillar of modern information security, safeguarding data, privacy, and communication in an increasingly interconnected world. With the advent of quantum computing and advanced persistent threats, traditional cryptographic systems face new challenges, necessitating innovative approaches. Artificial Neural Networks (ANNs), with their adaptability, learning capabilities, and computational efficiency, have emerged as a transformative tool in cryptography. This paper explores the applications of ANNs in key cryptographic domains, including key generation, encryption and decryption, and cryptanalysis. By leveraging their capacity to learn intricate patterns and optimize processes, ANNs offer significant advantages over traditional methods, such as enhanced randomness, scalability, and dynamic adaptability. However, their integration into cryptographic systems is not without challenges. Issues of interpretability, computational overhead, and susceptibility to adversarial attacks pose significant barriers to their widespread adoption. This study examines these challenges in detail and outlines promising future research directions, such as the integration of ANNs with post-quantum cryptography, the development of explainable AI models, and the enhancement of adversarial resilience. By bridging the fields of machine learning and cryptography, this research aims to contribute to the development of secure, efficient, and robust cryptographic systems that address the evolving demands of the digital age.