AI-BASED POST-QUANTUM CRYPTOGRAPHIC KEY EXCHANGE PROTOCOLS

Abstract

One of the promising solutions to overcome this quantum computing attack on conventional encryption protocol is the integration of Artificial Intelligence (AI) with Post 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 and MDPC) so as to enhance their resilience in IoT and distributed systems. The advanced of these allows us to provide a solid foundation for implementing security communication networks in the post quantum era.