Advances and Vulnerabilities in Modern Cryptographic Techniques: A Comprehensive Survey on Cybersecurity in the Domain of Machine/Deep Learning and Quantum Techniques

Abstract

In the contemporary landscape, where a huge amount of data plays a vital role, the importance of strong and robust cybersecurity measures has become increasingly paramount. This research proposes a review and extensively explores cybersecurity techniques within the domain of machine/deep learning and quantum techniques, with a particular focus on cryptographic methods and methodologies applied to image encryption. The proposed survey covers a range of cybersecurity techniques, including quantum random number generation, secure transmission of quantum images, watermarking through quantum methods, and quantum steganography. Moreover, it explores the domain of image encryption, which integrates adversarial neural networks, deep learning and machine learning, transformation techniques, and chaotic neural networks that can be used to secure digital data from cyber attacks. Our focus extends beyond highlighting advances in investigating vulnerabilities in existing cryptographic techniques. By identifying the challenges and weaknesses, the potential solutions are also presented, establishing a foundation for future recommendations. These future suggestions address and overcome the vulnerabilities observed in existing cybersecurity techniques. The aim of the extensive survey and analysis of existing cryptographic techniques is to provide a deep understanding of innovative and diverse approaches within the cybersecurity domain. Simultaneously, it aims to create a roadmap for the future to counter potential cyber threats and challenges.

Keywords

• Cybersecurity
• machine learning
• deep learning
• quantum cryptography
• cryptographic techniques