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
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
Classical cryptographic have a threat because of the emergence of quantum computing, and hence there is an urgent need to research post-quantum cryptography. To evaluate the potential of post-quantum encryption algorithms in countering quantum threats, here the study sees how they can be integrated with artificial intelligence (AI) and machine learning (ML). For their theoretical quantum resistance, the study looks at established post-quantum cryptography methods such as lattice-based, hash-based, and multivariate cryptographic schemes. A review of current research also simulation-based research is also taken into account to review the potential of AI/ML in bolstering security protocols and simplifying encryption processes. The key takeaway is that machine learning and AI do have actual potential to enhance post-quantum encryption through increasing its efficiency, flexibility, and resistance to future quantum attacks. AI also assists in real-time responsiveness in avoiding possible vulnerabilities. Our work contributes to the growing literature by combining insights from cryptographic and AI/ML research, providing a comprehensive evaluation of post-quantum encryption methods. This study emphasizes the highest importance of adopting hybrid cryptography techniques in order to advance information security in the quantum era, leading to further research in practical applications.
The accelerated advancements in quantum computing substantially threaten classical cryptographic systems, particularly those that rely on RSA, ECC, and other public key encryption methods. Particularly Shor's method shows how easily conventional cryptographic techniques might be broken; therefore quick development of quantum resistant substitutes is necessary. Rising as a potential answer with cryptographic algorithms meant to resist quantum attacks is post quantum cryptography (PQC). Nonetheless, performance trade-offs, compatibility problems, and the absence of standardized implementations make the shift to completely quantum-safe cryptographic systems very difficult. Hybrid PQC systems that combine conventional and quantum resistant cryptographic mechanisms have been suggested as a workable method for tackling these difficulties so guaranteeing security in the post quantum future. Emphasizing their security, performance, and practical implementations, this review article offers a thorough study of hybrid PQC methods. The resistance of many hybrid cryptographic models including lattice-based, code-based, and multivariate cryptographic techniques against quantum attacks is comprehensively investigated in this work methodologically, we review current PQC hybrid models, evaluate their computing efficiency, and examine security criteria grounded on recent research and standardizing initiatives like those under NIST. Our results show that hybrid PQC models create difficulties in terms of key management, computational overhead, and implementation complexity even if they improve security by reducing risks related with both conventional and quantum attacks. A comparative study shows among several hybrid systems the trade-offs in terms of encryption speed, key sizes, and resource use. Moreover showing the feasibility of PQC hybridizing in IoT, cloud computing, and block chain security are pragmatic uses of PQC hybridizing in which case appropriate protocols are needed for broad acceptance. At last, our work emphasizes the significant contribution hybrid PQC models provide in the transitional period producing a quantum secure environment. To enable basic integration into the present digital infrastructure, future research should focus on improving the efficiency of hybrid cryptosystems, optimizing key exchange mechanisms, and regulating standardization concerns. The findings of this study support the present discussion on post quantum security and provide data for academics and corporate leaders aiming for the next generation of cryptographic solutions. https://doi.org/10.5281/zenodo.17873992
Abstract : Objective: The rapid development of quantum computing poses an existential threat to classical cryptographic systems that currently secure global digital infrastructure. In direct response to this quantum threat, post-quantum cryptography (PQC) has emerged as a critical field dedicated to designing algorithms resistant to quantum attacks. Simultaneously, artificial intelligence (AI) — particularly machine learning (ML) and deep learning (DL) — has demonstrated promising and emerging capabilities across cybersecurity domains, including cryptography. Methods: This systematic review was conducted by searching IEEE Xplore, ACM Digital Library, Springer Link, Google Scholar, Scopus, and Web of Science using targeted keywords related to AI and PQC, covering literature published between 2015 and 2025. A total of 62 peer-reviewed studies meeting predefined inclusion criteria were analysed. Results: A total of 38 key studies were identified and analysed across four principal application domains: algorithm design and parameter optimization (31.6%), cryptanalysis and security assessment (26.3%), side-channel attack detection and defense (23.7%), and secure deployment on resource-constrained devices (18.4%). Practical case studies demonstrate measurable performance gains, including a 27% reduction in key exchange time reported in a specific study [60] and 98.3% accuracy in side-channel attack detection reported in a specific study. Conclusions: The synergy between AI and PQC represents a pivotal frontier in cybersecurity. This review provides a structured foundation for future interdisciplinary research in quantum-safe intelligent systems and identifies explainable AI (XAI) integration as the most critical open research direction.
Objective: The rapid development of quantum computing poses an existential threat to classical cryptographic systems that currently secure global digital infrastructure. In direct response to this quantum threat, post-quantum cryptography (PQC) has emerged as a critical field dedicated to designing algorithms resistant to quantum attacks. Simultaneously, artificial intelligence (AI) — particularly machine learning (ML) and deep learning (DL) — has demonstrated promising and emerging capabilities across cybersecurity domains, including cryptography. Methods: This systematic review was conducted by searching IEEE Xplore, ACM Digital Library, Springer Link, Google Scholar, Scopus, and Web of Science using targeted keywords related to AI and PQC, covering literature published between 2015 and 2025. A total of 62 peer-reviewed studies meeting predefined inclusion criteria were analysed. Results: A total of 38 key studies were identified and analysed across four principal application domains: algorithm design and parameter optimization (31.6%), cryptanalysis and security assessment (26.3%), side-channel attack detection and defense (23.7%), and secure deployment on resource-constrained devices (18.4%). Practical case studies demonstrate measurable performance gains, including a 27% reduction in key exchange time reported in a specific study [60] and 98.3% accuracy in side-channel attack detection reported in a specific study. Conclusions: The synergy between AI and PQC represents a pivotal frontier in cybersecurity. This review provides a structured foundation for future interdisciplinary research in quantum-safe intelligent systems and identifies explainable AI (XAI) integration as the most critical open research direction.
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.
With the fast-paced development of quantum computing, classical cryptographic techniques are under severe security threats, prompting the need for post-quantum cryptographic mechanisms for secure data exchange in next-generation network communications. Post-quantum cryptography seeks to protect sensitive data against quantum attacks to provide long-term data confidentiality. But all current cryptographic techniques, including RSA and ECC, are susceptible to quantum algorithms like Shor's algorithm, which can factor large integers efficiently and break discrete logarithm problems, making classical cryptography useless. There is thus a pressing need for quantum attack-resistant encryption mechanisms. To meet these challenges, we introduce the Lattice-Based Cryptography with Hybrid Key Exchange (L-CHKE) framework, which combines lattice-based encryption with a hybrid key exchange mechanism. Lattice-based cryptography provides quantum resistance because it is based on hard mathematical problems such as Learning with Errors (LWE), which provides greater security and efficiency. The designed L-CHKE algorithm strengthens secure key distribution and authentication for next-generation networks, especially for applications needing strong security like financial transactions, IoT, and military communications. Experimental results verify that L-CHKE noticeably boosts computational efficiency by 98.45%, decreases key exchange latency by 42.56%, and guarantees robustness by 97.64% against quantum attacks, indicating it is an appropriate solution for post-quantum secure communication.