A Comparative Study of Public Network Intrusion Detection Hybrid Machine Learning Approaches

Abstract

Security in public networks has always been a challenge. With an ever-expanding landscape of cyberattacks, it is imperative to explore new/alternate mechanisms of implementing intrusion detection. Machine learning and deep learning techniques have emerged as promising for intrusion detection in the recent past, with increased efficiency. This study explores machine learning algorithms, namely Random Forest, Naive Bayes, and Decision Tree, for the detection of web-based attacks in public networks, using a combination of Principal Component Analysis and Haar Wavelet Transform for feature extraction. The results of these models are compared, and related issues and approaches to alleviate them are explored.