A MATHEMATICAL FRAMEWORK FOR CRYPTOGRAPHY USING GRAPH-THEORETIC MODELS AND FUZZY INFERENCE IN MACHINE LEARNING
Keywords:
Cryptography, Graph Theory, Fuzzy Inference, Machine Learning, Secure Communication, Encryption, Key Management, Neural Networks, Mathematical ModelingAbstract
This paper proposes an integrated mathematical framework that utilizes graph-theoretic models and fuzzy inference mechanisms within a machine learning context to enhance the security, adaptability and intelligence of cryptographic systems. Graph theory aids in representing and analyzing complex cryptographic structures such as key distribution networks, while fuzzy logic introduces reasoning under uncertainty essential for adaptive cryptographic decision-making. By embedding these methodologies into machine learning workflows, particularly supervised and unsupervised learning algorithms, we demonstrate improved performance in encryption, key management and intrusion detection. Experimental simulations validate the effectiveness of this hybrid framework in securing data transmission in dynamic environments such as IoT and cloud infrastructures.
