AI-Powered Driven Intrusion Systems in Cyber Security and Zero-Day Attack Detection

AI-powered intrusion systems are becoming essential as cyber threats grow more complex and unpredictable. This review examines how modern artificial intelligence techniques strengthen intrusion detection by learning attack patterns, adapting to new behaviors, and identifying previously unknown vulnerabilities. The paper highlights the role of machine learning, deep learning, and hybrid intelligent models in improving detection accuracy, reducing false alarms, and enhancing real-time analysis. Special attention is given to the challenge of zero-day attacks, where no prior signatures exist; AI-based models address this gap by focusing on anomaly recognition and behavior-driven insights. The review also explores recent developments, comparative performance trends, and practical limitations that influence deployment in real-world networks. Overall, the study underscores how AI-driven intrusion detection systems provide a proactive defense layer capable of evolving with emerging threats and supporting more resilient cybersecurity operations.