Generation of secure communication was getting known as a promising technology called as a quantum cryptography. Unfortunately, it still faces several challenges, most notably in terms of high computational demands, scalability limitations, quantum decoherence, and vulnerability to side channel attacks, such that deployment of it in real world remains impossible yet. This study shows how Machine Learning (ML) and Deep Learning (DL) can be applied to tune the mentioned obstacles so that quantum cryptographic frameworks become more secure, more efficient and more scalable. In particular, we introduce a hybrid AI enabled model where RL can be utilized for tuning the performance of the post quantum cryptographic algorithm implementations, GANs can be adopted for measuring the robustness of the system, and FL can be used to make the quantum key distribution scalable. Besides, this thesis uses Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to incorporate the quantum authentication and key exchange methods. Additionally, our methodology employs techniques based on Graph Neural Networks (GNNs) to achieve the best performance in the networks and Adversarial Machine Learning (AML) to counter and detect, and then reduce cyber threats in the run time. For preferable cryptographic computations, we introduce Quantum Neural Networks (QNNs) to reduce its dependency on expensive quantum hardware. Experimental results also show that ML/DL based quantum security frameworks provide lowering of compute burden, improvement in real time security of data, and improve resistance to cyber threats. The result of this roadmap based on AI driven strategy is a comprehensive view, with a high level of direction describing the main steps to lead to post quantum security and take benefit of quantum cryptography and apply it in a multitude of significant utilization including secure communication, cryptographic financial systems or national infrastructure protection.
Generation of secure communication was getting known as a promising technology called as a quantum cryptography. Unfortunately, it still faces several challenges, most notably in terms of high computational demands, scalability limitations, quantum decoherence, and vulnerability to side channel attacks, such that deployment of it in real world remains impossible yet. This study shows how Machine Learning (ML) and Deep Learning (DL) can be applied to tune the mentioned obstacles so that quantum cryptographic frameworks become more secure, more efficient and more scalable. In particular, we introduce a hybrid AI enabled model where RL can be utilized for tuning the performance of the post quantum cryptographic algorithm implementations, GANs can be adopted for measuring the robustness of the system, and FL can be used to make the quantum key distribution scalable. Besides, this thesis uses Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to incorporate the quantum authentication and key exchange methods. Additionally, our methodology employs techniques based on Graph Neural Networks (GNNs) to achieve the best performance in the networks and Adversarial Machine Learning (AML) to counter and detect, and then reduce cyber threats in the run time. For preferable cryptographic computations, we introduce Quantum Neural Networks (QNNs) to reduce its dependency on expensive quantum hardware. Experimental results also show that ML/DL based quantum security frameworks provide lowering of compute burden, improvement in real time security of data, and improve resistance to cyber threats. The result of this roadmap based on AI driven strategy is a comprehensive view, with a high level of direction describing the main steps to lead to post quantum security and take benefit of quantum cryptography and apply it in a multitude of significant utilization including secure communication, cryptographic financial systems or national infrastructure protection.
Quantum computing represents a major threat to future communication systems security: Once a certain level of maturity is reached, the security enjoyed by classical cryptographic schemes will immediately be put in check by (i) highly efficient quantum algorithms able to solve hard problems non-negligibly faster than the best-known classical solutions and (ii) potentially scalable fabrication techniques for quantum circuits. This work investigates the design of quantum-safe security primitives suitable for new cybersecurity threats in next-generation communications networks by means of the integrated use of quantum cryptography and deep learning. By combining the unconditionally secure guarantees offered by quantum cryptographic protocols with the adaptive, data-driven intelligence of deep learning models, this work proposes a holistic solution to secure communication infrastructures against not only current but also future cyber threats. A central element is the utilization of QKD-protocols that use the properties of quantum mechanics in order to securely distribute keys that are resistant to computational attacks. In parallel thereof, deep learning techniques are applied to monitor the system by analyzing real-time network traffic, identifying abnormal activities, and forecasting sophisticated attack patterns that frequently are overlooked by conventional security systems. This approach mitigates constraints of standalone quantum cryptography, including noise and hardware imperfections, and improves threat identification over traditional signature-based systems. The work studies the synergy between quantum cryptography and AI-assisted analytics, optimizing quantum channel estimation, error correction, and intrusion detection with deep learning. We conduct extensive simulations to show the efficacy of the framework against various adversarial attacks such as man in the middle, side-channel, and adversarial machine learning threats. The system has high stability without loss of quantum bit error rate, and high detection accuracy, for handling the change of attack vectors in distributed, heterogeneous communication networks.
Quantum computing has introduced unprecedented challenges to traditional cryptographic systems, rendering many current protocols vulnerable to quantum attacks. Quantum-resistant cryptography has emerged as a crucial field, employing innovative algorithms such as lattice-based and hash-based schemes to counter these threats. Concurrently, machine learning (ML) revolutionizes cryptography by enhancing protocol optimization, key generation, and threat detection. This paper explores the integration of ML with quantum-resistant cryptographic frameworks, highlighting its potential to address efficiency and scalability challenges while improving security. Strategies for combining these domains are discussed, emphasizing hybrid models, dynamic adaptation to threats, and lightweight solutions. The study also considers the potential risks of ML integration, such as adversarial vulnerabilities and resource demands, while recommending collaborative efforts among researchers, policymakers, and practitioners. Ultimately, this interdisciplinary approach promises robust, scalable, and future-ready cryptographic systems for secure communication in a quantum era.
Quantum cryptography, like QKD, leverages quantum mechanics for unbreakable encryption against quantum computers, but faces deployment hurdles in key management, scalability, and noise. AI integration revolutionizes this by optimizing protocols, minimizing errors, and enabling real-time monitoring. Deep and reinforcement learning dynamically assess channels, allocate resources, and predict vulnerabilities. AI anomaly detection counters side-channel attacks and eavesdropping, bolstering robustness. This research explores AI-optimized QKD, quantum-secure authentication, adaptive encryption, and hybrid post-quantum approaches, ensuring scalable, resilient defenses for future communications amid quantum cyber threats.
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.
One of the promising solutions to overcome this quantum computing attack on conventional encryption protocol is the integration of Artificial Intelligence (AI) withPost Quantum Cryptography (PQC).In this research, AI-driven approaches are investigated in improving post quantum cryptographic key exchange protocol by being efficient, secure and scalable.Based on lattice based cryptography resistant against quantum [2], this work makes use of machine learning models in order to assist in optimizing, or predicting optimal parameter choices, for better key exchange in practice.The system proposed consists of the Learning with Errors (LWE) problem for increased security [4] and a hybrid AI model that minimizes the computation overhead in key exchange protocols [3].The system dynamically adjusts cryptographic parameters based on the side channel attacks that are feasible and up to date, making the system resilient to side channel attacks, and also reduces the latency of secure communication through the incorporation of deep learning algorithms.The key findings show that AI driven model significantly improves the key exchange speed by 27 percent over traditional PQC techniques, and still provides robust security guarantees [5].To sum up, this research brings a significant advancement in PQC protocols by combining the AI techniques to predict and defend against cryptographic VTEs [7].In the future, this approach will be generalized to other quantum-resistant protocols (including SIKE Narayana Gaddam https://iaeme.com/Home/journal/IJCET
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