Synergizing AI and Cybersecurity: A New Methodology to Real-Time Intrusion Detection and Prevention System

The growing complexity of cyber threats necessitates creative solutions beyond conventional rule-based security systems. This study presents a new method for the incorporation of artificial intelligence (AI) into intrusion detection and prevention systems (IDPS) that facilitates real-time threat mitigation, adaptive learning, and autonomous response. Through the use of machine learning (ML), behavioural analytics, and generative AI, this solution overcomes the weaknesses of legacy systems while maximizing accuracy, scalability, and operational efficiency in cybersecurity. Key Words: Artificial Intelligence (AI); Cybersecurity; Intrusion Detection System (IDS); Intrusion Prevention System (IPS); Real-Time Threat Detection; Machine Learning (ML); Anomaly Detection; Behavioural Analytics; User and Entity Behaviour Analytics (UEBA); Automated Incident Response; Adaptive Learning; Generative Adversarial Networks (GANs); Adversarial AI; Zero-Day Attack Detection; Security Orchestration, Automation, and Response (SOAR); Autonomous Cybersecurity; Cyber Threat Mitigation; Network Security; Deep Learning; Explainable AI (XAI); Federated Learning; Threat Intelligence; Real-Time Analytics; Predictive Cybersecurity; AI-Driven Defence Systems; Proactive Defence Mechanisms; Self-Improving Systems; Quantum-Resistant AI; Zero Trust Architecture; Cyber-Physical Systems Security