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

N. S. Vasantha; R. Sukumar; Shridhar Allagi; Tessy Tom

doi:10.48084/etasr.17560

Authors

N. S. Vasantha Department of Electronics and Communication Engineering, School of Engineering and Technology, Jain University, Bangalore, Karnataka, India
R. Sukumar Centre of Research for Cyber Security (CRCE), School of Engineering and Technology, Jain University, Bangalore, Karnataka, India
Shridhar Allagi Department of Computer Science and Engineering, KLE Institute of Technology, Hubballi, Karnataka, India
Tessy Tom Centre of Research for Cyber Security (CRCE), School of Engineering and Technology, Jain University, Bangalore, Karnataka, India

Volume: 16 | Issue: 4 | Pages: 37252-37257 | August 2026 | https://doi.org/10.48084/etasr.17560

Received: 15 January 2026 | Revised: 11 April 2026 and 22 April 2026 | Accepted: 23 April 2026 | Online: 7 June 2026

Corresponding author: N. S. Vasantha

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.

Keywords:

Principal Component Analysis (PCA), Random Forest (RF), Machine Learning (ML), Intrusion Detection System (IDS)

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