Smart Hybrid Intelligent Encryption and Learning Defense an Adaptive Hybrid Ml-Cryptographic Framework

Abstract

The increase in the number and intricacy of cyberattacks is generating immensely difficult security environs for modern cryptographic systems that have already been challenged by cyberattacks, side-channel attacks, and now face the approaching threat of quantum computing. Traditional symmetric and asymmetric encryption algorithms can theoretically provide a high level of security from a mathematical standpoint, but they do not provide the necessary adaptive capabilities required to dynamically respond to the changing threat environment. In this paper, we propose a novel hybrid cryptographic framework called SHIELD (Smart Hybrid Intelligent Encryption and Learning Defense) that combines Machine Learning (ML) techniques with classical cryptographic primitives to implement adaptive and context-aware encryption. The SHIELD framework embodies a erratic Forest separator to classify real-time threats, a Convolutional Neural Network to detect a bizarre pattern in ciphertext traffic, and uses AES 256 and RSA 4096 as the foremost cryptographic engines. Additionally, the framework actively adjusts the key lengths, encryption modes, and algorithm option based on the recognized threat outline of the network environment. Experimental assessment on three datasets, NSL KDD, CICIDS 2017, and synthetic data construct from a custom generator, exemplify an overall accuracy rate of 98.7% for the SHIELD framework to classify threats, 23.4% lower than standard latency structure, and increased protest to known side-channel attacks. The framework was also significantly evaluated for multiple performance metrics and evaluated against other state-of-the-art techniques employ Precision, Recall, F1-Score, ROC-AUC, and computational overhead metrics.