Intrusion Detection Systems Using Machine Learning.

Abstract

The utilization of machine learning to enhance Intrusion Detection Systems (IDS). It encompasses an exploration of diverse IDS categories, fundamental evaluation metrics, and the dynamic landscape of machine learning methodologies. Recent trends underscore a shift towards the adoption of deep learning techniques for improving attack detection capabilities. Challenges arise from heightened model complexity and increased resource requirements. The paper also suggests future directions that encompass the development of updated datasets and the efficient management of resources through cloud integration. Throughout, this study emphasizes the continuous demand for research and innovation in the field of cybersecurity.