Intelligent Intrusion Detection Using Signature Analysis and Predictive Cognitive Threat Engine

The fast development of the network traffic and the dynamism of contemporary communication systems have generated an urgent requirement of intelligent structures that have the ability to detect, analyze and foresee cyber threats rightly. Conventional intrusion detection and classification strategies tend to fail with massive network data under adversarial conditions as well as that changing with time. To improve security, accuracy, and predictive power, the proposed paper will present a new multi-stage cyber threat framework based on deep learning, quantum-inspired modeling, evolutionary optimization, and graph-based reasoning. Adversarial-Aware Cognitive Data Refinement (AA-CDR) is initially used to preprocess network data to remove noise, save adversarial exemplification, and reconstruct minority attack examples to produce attack-aware datasets. The Quantum-Enhanced Temporal Behaviour Modeling (QETBM) represents both short and long-term node behaviour of sequential traffic. Causally significant and discriminative features are then determined by Causal Evolutionary Feature Synthesis (CE-FS). Graph-Centric Attack Reasoning Classifier (GCARC) relates structural graph relationships to identify normal and malicious activities, whereas the Predictive Cognitive Threat Intelligence Engine (PCTIE) predicts possible threats. The evaluations on the experiments indicate considerable advancement in detection accuracy, precision, recall, feature reduction, and computational efficiency over the traditional ML-AIDS, ANN and PCA methods and thus the framework has been demonstrated to be highly adaptable to dynamic and large-scale network security settings.