AI-DRIVEN INTRUSION DETECTION SYSTEMS FOR SMART NETWORK SECURITY

Abstract

As smart networks grow in complexity and scale, the need for advanced security mechanisms becomes critical. Traditional intrusion detection systems (IDS) often struggle to cope with the scale and sophistication of attacks in these environments. Artificial Intelligence (AI) techniques, particularly machine learning (ML) and deep learning (DL), offer promising solutions for enhancing the detection and mitigation of intrusions in smart network environments. This paper explores the role of AI-driven IDS in smart network security, examining various machine learning and deep learning techniques, their applications, and the challenges in deploying them. We present a comparative analysis of AI-based IDS with traditional systems, highlight the effectiveness of AI-driven models in real-time threat detection, and outline the potential future directions for research. The article also discusses the integration of AI systems with network infrastructures and presents two conceptual charts for performance comparison.