INTEGRATING FUZZY LOGIC AND GRAPH THEORY WITH DEEP LEARNING FOR SECURE CRYPTOGRAPHIC SYSTEMS

Abstract

Cryptographic systems form the backbone of secure communication in the digital era. With the rapid advancement of data processing and cyber-attack strategies, conventional cryptographic approaches face limitations in scalability and adaptability. This paper proposes a novel integration of fuzzy logic, graph theory, and deep learning to design adaptive, robust, and intelligent cryptographic mechanisms. Fuzzy logic facilitates uncertainty handling and rule-based adaptability, graph theory provides structural modeling for cryptographic networks, and deep learning enhances pattern recognition and key generation efficiency. We explore fuzzy-based secure key management, graph-theoretic encryption schemes, and neural architectures for anomaly detection in cryptographic channels. The synergy of these paradigms paves the way for designing cryptographic systems capable of learning, adapting, and defending against evolving security threats. Performance evaluations demonstrate the proposed hybrid framework’s superiority in terms of security entropy, resistance to attacks, and computational efficiency, laying the foundation for future AI-driven cryptography.