An Intelligent Intrusion Detection System for Network Security Using Machine Learning

Abstract

As cyber threats become increasingly sophisticated, the need for
intelligent and adaptive security mechanisms has become more crucial than ever.
Traditional intrusion detection systems (IDS) struggle to identify novel or evolving attacks
due to their reliance on static rules and signatures. This research proposes a machine
learning-based IDS designed to detect anomalous behavior in network traffic. Using
supervised and unsupervised learning models such as Random Forest, Support Vector
Machine (SVM), and Autoencoders, the system is trained and tested on benchmark
datasets including NSL-KDD and CICIDS2017. The proposed IDS demonstrates high
accuracy and robustness in identifying multiple types of attacks and offers a real-time
detection interface for practical deployment.