COGNITIVE AND ADAPTIVE CRYPTOGRAPHY: DEEP LEARNING'S ROLE IN DRIVEN INTELLIGENT CIPHER SYSTEMS

The increasing sophistication of cyber-attacks necessitates advanced cryptographic frameworks that can evolve dynamically to respond to evolving attack vectors. Available cryptographic mechanisms, including symmetric and asymmetric cryptography, homomorphic cryptography, and quantum-resistant cryptography, are premised on predetermined algorithms and static security parameters. These approaches, however, have limited adaptability, real-time threat detection, and adversarial attack resilience. Besides, conventional encryption modelsface key management complexity, computational expense, and vulnerability to machine learning-based cryptanalysis. In addressing the issues, this study proposes a Cognitive and Adaptive Cryptographic (CAC) system founded on Deep Learning (DL) techniques for intelligent cipher systems. The proposed model integrates Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for adaptive key generation, Generative Adversarial Networks (GANs) for attack detection and cryptanalysis, and Reinforcement Learning (RL) for policy optimization in terms of real-time threat appraisal. This adaptive approach enhances security by independently tuning encryption parameters according to detected anomalies, substantially reducing the risk of cryptographic attacks. The process entails dataset collection from encrypted communication records and simulated attacks, real-time anomaly detection model training, and performance analysis using key entropy, encryption rate, and computational efficiency as metrics. Experiment results indicate that the proposed CAC framework is more robust to adversarial attacks with 97.2% accuracy, computationally efficient, and encryptable compared to the conventional cryptographic techniques.