Intrusion Detection System Using Machine Learning

Abstract

In the evolving landscape of cybersecurity, traditional rule-based intrusion detection systems (IDS) struggle to keep pace with the increasing volume, velocity, and sophistication of network attacks. This paper explores the design and implementation of an intrusion detection system that leverages machine learning (ML) techniques to enhance threat detection capabilities. By analyzing network traffic data and identifying patterns indicative of malicious behavior, ML-based IDS solutions offer improved accuracy, adaptability, and automation in identifying both known and unknown threats. The proposed system employs supervised and unsupervised learning algorithms, including Decision Trees, Random Forests, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Neural Networks. Feature selection and data preprocessing are applied to optimize model performance.