Advancing Cyber Defense: Machine Learning Techniques for NextGeneration Intrusion Detection

Abstract

The rapid evolution of cyber threats has made traditional intrusion detection systems (IDS) increasingly ineffective in addressing sophisticated attacks. To combat this challenge, the integration of machine learning (ML) techniques into intrusion detection systems has emerged as a promising solution. This paper explores the potential of ML-driven approaches for advancing cyber defense, focusing on next-generation IDS that can intelligently detect and mitigate a wide range of cyber threats. By leveraging the power of algorithms such as supervised learning, unsupervised learning, and deep learning, these systems can autonomously analyze vast amounts of network data, identify hidden patterns, and adapt to new attack strategies. We review key ML models and their applications in intrusion detection, highlighting their strengths, limitations, and the challenges associated with training models on diverse datasets. Additionally, we examine hybrid models that combine multiple ML techniques to enhance detection accuracy and reduce false positives. As organizations continue to face increasingly complex and dynamic cyber threats, machine learning offers a crucial advantage in building adaptive, scalable, and effective intrusion detection systems. This paper aims to provide insights into the latest advancements in ML-based IDS and their potential role in shaping the future of cybersecurity defense strategies.