Enhancing Network Security: Machine Learning Approaches for Intrusion Detection

Abstract

This paper delves into the critical role of machine learning in bolstering network security through effective intrusion detection systems (IDS). It outlines the escalating threats in cyberspace and the necessity for advanced techniques to counter them. By harnessing the power of machine learning algorithms, the paper highlights the potential to enhance the accuracy and efficiency of intrusion detection, thereby fortifying network defenses against evolving cyber threats. Through a comprehensive review of existing literature and methodologies, the abstract underscores the significance of leveraging machine learning for proactive threat detection and response, ultimately contributing to a more resilient and secure network infrastructure.