The Rise of AI-Driven Network Intrusion Detection Systems: Innovations, Challenges, and Future Directions

Abstract

The integration of Artificial Intelligence (AI) into Intrusion Detection Systems (IDS) represents a significant advancement in cybersecurity, addressing the increasing complexity and frequency of cyber threats. AI-driven IDS utilize machine learning and deep learning algorithms to analyze vast amounts of network traffic, identifying anomalies and potential intrusions in real time. This capability enhances the detection of both known and unknown threats, significantly reducing false positives compared to traditional systems. As organizations face evolving attack vectors, the need for adaptive and scalable security solutions becomes paramount. Despite their advantages, AI-based IDS face challenges such as data quality management, the requirement for extensive training datasets, and the risk of false negatives. Continuous research is essential to refine these systems and improve their effectiveness. Future directions include integrating threat intelligence for more proactive detection, enhancing automation in incident response, and developing robust frameworks to tackle zero-day vulnerabilities. The evolution of AI in IDS not only strengthens organizational defenses but also plays a crucial role in compliance with regulatory standards, making it an indispensable component of modern cybersecurity strategies