Abstract Cryptanalysis is crucial for securing cryptographic systems, particularly with the advent of quantum computing, which threatens traditional encryption methods. Advanced cryptanalytic techniques are essential for developing robust systems that can withstand quantum attacks, ensuring encrypted data remains secure and accessible only to authorized parties. This paper introduces the Quantum Hopfield Neural Network (QHopNN) as a novel approach to enhance key recovery in symmetric ciphers. This research provides valuable insights into integrating quantum principles with neural network architectures, paving the way for more secure and efficient cryptographic systems. By leveraging quantum principles like superposition and entanglement, along with Hopfield networks’ pattern recognition and optimization capabilities, QHopNN achieves superior accuracy and efficiency in deciphering encrypted data. Additionally, integrating unitary quantum evolution with dissipative dynamics further enhances the cryptographic robustness and efficiency of QHopNN. The proposed framework is rigorously evaluated using prominent symmetric ciphers, including S-AES and S-DES, and benchmarked against existing state-of-the-art techniques. Experimental results compellingly demonstrate the superiority of QHopNN in key recovery, with a mean Bit Accuracy Probability (BAP) of 0.9706 for S-AES and 0.9815 for S-DES, significantly outperforming current methods. This breakthrough opens new avenues for advancing cryptanalysis and sets the stage for pioneering future research in quantum-inspired cryptographic techniques.
Abstract Cryptanalysis is crucial for securing cryptographic systems, particularly with the advent of quantum computing, which threatens traditional encryption methods. Advanced cryptanalytic techniques are essential for developing robust systems that can withstand quantum attacks, ensuring encrypted data remains secure and accessible only to authorized parties. This paper introduces the Quantum Hopfield Neural Network (QHopNN) as a novel approach to enhance key recovery in symmetric ciphers. This research provides valuable insights into integrating quantum principles with neural network architectures, paving the way for more secure and efficient cryptographic systems. By leveraging quantum principles like superposition and entanglement, along with Hopfield networks’ pattern recognition and optimization capabilities, QHopNN achieves superior accuracy and efficiency in deciphering encrypted data. Additionally, integrating unitary quantum evolution with dissipative dynamics further enhances the cryptographic robustness and efficiency of QHopNN. The proposed framework is rigorously evaluated using prominent symmetric ciphers, including S-AES and S-DES, and benchmarked against existing state-of-the-art techniques. Experimental results compellingly demonstrate the superiority of QHopNN in key recovery, with a mean Bit Accuracy Probability (BAP) of 0.9706 for S-AES and 0.9815 for S-DES, significantly outperforming current methods. This breakthrough opens new avenues for advancing cryptanalysis and sets the stage for pioneering future research in quantum-inspired cryptographic techniques.
Quantum computers offer a novel approach to data processing, which poses both opportunities and threats for modern cryptography. While Shor’s algorithm for prime factorisation demonstrated the possible impact of quantum cryptanalysis, the complete quantum significance in the cryptanalytical context still needs further research. This thesis compiles a series of recent publications, each of which examines a distinct application of quantum advantage in cryptography. Collectively, these contributions constitute a non-exhaustive compendium of recent advancements in quantum cryptanalysis. We first examine applications of the Harrow-Hassidim-Lloyd (HHL) algorithm for solving linear equation systems. We propose two approaches in which the quantum advantage of the HHL algorithm could be leveraged to investigate structural properties relevant to symmetric cryptanalysis. In the subsequent chapter, an analysis is conducted on the significance of adiabatic quantum computing and its role in the cryptanalytical setting. Together, these results offer novel insights into the capabilities and limitations of quantum-enhanced cryptanalysis of symmetric ciphers. Turning to asymmetric settings, two quantum attacks are proposed that result in the complete break of the underlying ciphers. The first one exploits the recipient’s encryption process to extract the secret key used in the Quantum Encryption Method. The second targets an Identity-Based Encryption scheme whose security relies on isogenies. We demonstrate that the distinct use of isogenies introduces new attack vectors for both classical and quantum adversaries. Collectively, these contributions deepen the understanding of quantum-enhanced cryptanalysis, thereby clarifying both its potential and current boundaries.
The rapid growth of Internet of Things (IoT)-enabled healthcare systems has led to the widespread use of cloud-based platforms for storing and transmitting medical images. While this paradigm improves accessibility and scalability, it also introduces significant security challenges, including unauthorized access, data breaches, and cyberattacks. Secure medical image cryptanalysis has therefore become a critical research area, focusing on evaluating and strengthening encryption techniques used in healthcare systems. Recent advancements in artificial intelligence, particularly deep learning and quantum neural networks (QNNs), have opened new avenues for enhancing medical image security. Quantum neural networks leverage principles of quantum computing such as superposition and entanglement to improve computational efficiency and encryption strength. Additionally, deep learning-based cryptographic techniques enable intelligent key generation, anomaly detection, and attack mitigation. This paper presents a comprehensive review of secure medical image cryptanalysis techniques using quantum neural networks in IoT-enabled cloud storage environments. The study explores hybrid approaches combining quantum-inspired algorithms, deep learning models, and cryptographic techniques to improve data confidentiality, integrity, and availability. Recent research highlights the effectiveness of quantum-enhanced encryption frameworks, such as quantum key distribution and hybrid cryptographic systems, in protecting sensitive medical data. Despite these advancements, challenges such as high computational complexity, limited quantum hardware availability, and scalability issues persist. This review analyses recent developments, identifies research gaps, and provides future directions for developing secure and efficient medical image cryptanalysis systems.
The advent of quantum computing presents a potential threat to conventional cryptographic systems, prompting the urgent need for post-quantum cryptography (PQC) solutions. However, the complexity of these cryptographic protocols often leads to inefficiencies in performance, hindering their widespread adoption. This paper explores the integration of machine learning (ML) techniques to optimize the performance of post-quantum cryptographic protocols. We propose an innovative framework where ML algorithms are leveraged to enhance the efficiency of key generation, encryption, decryption, and signature processes in PQC schemes. By utilizing data-driven approaches such as reinforcement learning and neural networks, we aim to reduce computational overhead and improve latency, while maintaining the robustness and security of post-quantum algorithms. Experimental results demonstrate significant performance improvements over traditional optimization methods, showcasing the potential of ML in advancing the practicality of PQC protocols. This work lays the foundation for a new class of cryptographic systems capable of withstanding quantum threats while maintaining high operational efficiency.
The rapid advancement of quantum computing poses a significant threat to traditional cryptographic systems that rely on computational hardness assumptions such as integer factorization and discrete logarithms. Quantum algorithms, particularly Shor's algorithm, have the potential to break widely used cryptographic standards including RSA and Elliptic Curve Cryptography. To address these emerging challenges, this research proposes a machine learning-enhanced post-quantum cryptographic framework designed to provide adaptive and resilient data security in the quantum computing era. The proposed framework integrates post-quantum cryptographic algorithms with intelligent machine learning mechanisms to improve the robustness of encryption systems against quantum-based attacks. The system incorporates three major components: a post-quantum encryption module, a machine learning–based anomaly detection module, and an adaptive key management system powered by reinforcement learning. The anomaly detection component utilizes neural network models to monitor encrypted data streams and identify abnormal patterns that may indicate potential decryption attempts. In addition, reinforcement learning dynamically adjusts cryptographic key parameters and rotation schedules to reduce the risk of key compromise. Experimental evaluation demonstrates that the proposed framework significantly improves detection accuracy, reduces key management latency, and enhances resilience against simulated quantum decryption attacks when compared with conventional cryptographic systems. The integration of machine learning enables the framework to dynamically adapt to evolving threats while maintaining strong encryption standards. The results highlight the potential of combining artificial intelligence with post-quantum cryptography to build adaptive, intelligent, and future-proof security systems capable of protecting sensitive data in the presence of powerful quantum computing technologies.\\n\\n
The emergence of quantum computing poses a significant threat to classical cryptographic systems, particularly those relying on mathematical hardness assumptions for key exchange. While symmetric algorithms such as the Advanced Encryption Standard (AES) remain efficient and widely deployed, their long-term security ultimately depends on the secrecy of keys. In this paper, we present a hybrid framework that integrates quantum key distribution (QKD) with AES and DES to enhance key security without altering cipher internals. Using the BB84 protocol as the quantum key generator, we implement a simulation-based system in Qiskit and evaluate it against classical pseudo-random key generation.Our methodology covers five phases: establishing AES/DES baselines, generating quantum random keys, implementing BB84 with basis reconciliation and error detection, integrating quantum keys with AES/DES, and benchmarking across varying key sizes and file sizes. Results demonstrate that AES encryption throughput remains stable (around 250–370 MB/s) regardless of key source, while QKD introduces measurable latency in key generation (around 40–110 seconds for 128–256 bits). Importantly, adversarial interference is reliably detected, with 97.3% accuracy in eavesdropper identification.The findings confirm that while classical systems excel in speed and scalability, QKD offers unmatched, physics-backed security assurances. Our study highlights the practicality of hybrid quantum-classical encryption, providing a foundation for secure communication in the post-quantum era.
This paper presents a novel approach to cryptanalysis leveraging the capabilities of quantum computing. The core idea involves utilizing quantum simulation to accelerate the mathematical computations inherent in modern cryptographic algorithms. Specifically, we explore the application of quantum phenomena, such as quantum entanglement and superposition, to fundamentally challenge the security assumptions underpinning these algorithms. The methodology outlines a framework for targeted attack strategies, emphasizing the potential for exponential speedups compared to classical methods. The theoretical underpinnings and potential impact of this approach are detailed, proposing a paradigm shift in how cryptographic systems are analyzed and defended. The ultimate goal is to demonstrate a viable path toward breaking currently deployed encryption standards.