Detecting Cyber-Attacks with Intrusion Detection Systems Exploring Machine Learning Approaches

Abstract

increase in internet usage has been paralleled by a surge in cyber-attacks, many of which are novel and necessitate advanced detection mechanisms. Intrusion Detection Systems amuse oneself is critical bit part within keep track of network congestion which identify malicious activity. The detection of emerging threats requires the development of models using vast amounts of data to effectively distinguish between normal and anomalous traffic patterns. This has led to the growing appeal of machine learning algorithms that intensify predictive rightness of Intrusion Detection Systems. However, the high dimensionality of data poses significant challenges, particularly the “curse of dimensionality,” which can degrade classification performance. This issue has prompted the adoption of feature selection and dimensionality reduction techniques to improve classification outcomes. In response to the increasing undivided attention about that application at supervised ML for Intrusion Detection Systems, this wrapper attending a sweeping look over of supervised learning algorithms along with their effectiveness in intrusion detection system. We review the core concepts of IDS, machine learning methodologies, and dimensionality reduction approaches. Additionally, we provide a detailed taxonomy that outlines the suitability of various algorithms for different IDS datasets, with a focus on the impact of feature selection on enhancing classification performance.