AI-Driven Network Intrusion Detection Systems: Enhancing Real-Time Threat Detection

Abstract

In an era where cybersecurity threats are evolving rapidly, traditional network intrusion detection systems (NIDS) are increasingly inadequate. This research paper explores the integration of Artificial Intelligence (AI) into NIDS to enhance real-time threat detection capabilities. By leveraging machine learning (ML) and deep learning (DL) techniques, AI-driven NIDS offer superior performance in detecting sophisticated and previously unknown threats. This paper reviews existing AI methodologies applied to NIDS, evaluates their effectiveness, and presents a framework for integrating AI to improve real-time threat detection.