AI For Predictive Cybersecurity in Website Traffic Analysis

Cyber attacks require adaptive and intelligent security measures.The following paper outlines a dualframework method: supervised machine learning-based Network Intrusion Detection System (NIDS) and AIassisted predictive analytics platform for web traffic security.NIDS uses Support Vector Machine (SVM) and Artificial Neural Network (ANN) as classifiers to identify DoS, probing, R2L, and U2R attacks utilizing datasets such as NSL-KDD and CICIDS2017.Data pre-processing, feature extraction, and model testing improve detection accuracy.In parallel, the AI-based solution incorporates System to scan for malicious traffic and anticipate attack vectors.A Zero-Trust Architecture (ZTA) is employed to authenticate and verify user and device identities in real-time.AI models are utilized for URL classification, threat analysis, and block/unblock action automation.Text authentication adds additional user access security.Performance metrics validate the dominance of the hybrid models over legacy systems.Hyperparameter tuning and deployment in edge/cloud environments enable real-time usage.Collectively, these AI-driven techniques provide an aggressive, scalable, and robust response to contemporary cybersecurity threats.