Intelligent Intrusion Detection Systems Using Machine Learning

Abstract

Intrusion Detection System (IDS) watches network traffic for fraudulent activity and gives immediate alerts when it is observed. By notifying security administrators of known or possible threats or by sending alerts to a centralized security tool, an intrusion detection system (IDS) can assist speed up and automate network threat detection. This paper presents a comprehensive overview of Intrusion Detection Systems (IDS) and the machine learning techniques commonly employed to enhance their detection capabilities. Various IDS approaches are examined, with a focus on both signature-based and anomaly-based models. The study highlights key machine learning methods—such as Support Vector Machines (SVM), Convolutional Neural Networks (CNN), Random Forests, and clustering algorithms—and evaluates their suitability for identifying malicious activities in network environments. The growing integration of AI in cybersecurity is also discussed, emphasizing its role in improving automated threat analysis and adaptive intrusion detection Additionally, the KDD-99 dataset is utilized to outline experimental procedures and demonstrate how these algorithms can be applied in practical IDS implementations. The findings emphasize the importance of selecting appropriate learning techniques to improve accuracy, reduce false positives, and strengthen overall network security.