Adaptive Machine Learning Algorithms for Intrusion Detection System in Cybersecurity

Abstract

The rapid advancement of cyber threats necessitates the development of more sophisticated intrusion detection systems (IDS). Traditional IDS methods of- ten fail to keep up with the evolving nature of these threats. This paper explores the use of adaptive ma- chine learning algorithms to enhance IDS performance in cybersecurity. By utilizing adaptive learning techniques, we aim to create a system that dynamically adjusts to new and emerging threats in real-time.

We evaluate several machine learning algorithms, including decision trees, support vector machines, neural networks, and ensemble methods, to determine their effectiveness in intrusion detection. Our pro- posed framework integrates these algorithms into an adaptive system that continuously improves its detection accuracy and reduces false positives by learning from ongoing threat patterns.

Experiments conducted on benchmark datasets re- veal that our adaptive IDS framework outperforms traditional methods, demonstrating significant improvements in detection accuracy and response time. This research highlights the potential of adaptive ma- chine learning algorithms to provide a more robust and intelligent defence against cyber threats, paving the way for next-generation intrusion detection systems.