Advanced AI-Powered Intrusion Detection System for Cybersecurity and Network Protection

Abstract

Modern computer networks face a wide range of threats, including denial-of-service attacks, port scanning, brute-force attempts, botnet activity, and previously unseen malicious behaviour. Traditional signature-based intrusion detection systems can identify known patterns but may struggle when attack behaviour changes or has no existing signature. This paper reviews how Artificial Intelligence (AI), machine learning (ML), and deep learning can strengthen intrusion detection and network protection. It presents a conceptual layered framework combining network-flow collection, data preparation, AI-based classification, anomaly detection, explainable alerts, and human-supervised response. The paper discusses commonly used approaches such as decision trees, random forests, support vector machines, neural networks, and anomaly-detection models, alongside evaluation measures including precision, recall, F1-score, false-positive rate, and detection latency. Public benchmark datasets such as CICIDS2017 can support controlled evaluation, but their limitations mean that results must not be assumed to transfer directly to live networks. This is a literature-based conceptual study; it does not claim to have trained or experimentally validated a new model. The paper concludes that an effective AI-powered IDS requires reliable data, careful validation, privacy protection, analyst oversight, and continuous monitoring