Adaptive Machine Learning Techniques for Proactive Intrusion Detection in Real-Time Networks

Abstract

Intrusion Detection Systems (IDS) are crucial for safeguarding real-time networks against unauthorized access and attacks. Traditional IDS face challenges in keeping pace with evolving threats, leading to a need for adaptive machine learning techniques. This paper explores the application of adaptive machine learning for proactive intrusion detection, highlighting its advantages in handling dynamic and complex network environments. We discuss various adaptive methodologies, including online learning, transfer learning, and reinforcement learning, and evaluate their effectiveness in real-time network scenarios. Empirical results demonstrate that adaptive models outperform static approaches, offering enhanced detection accuracy and response time.