Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2026.085248
Journal Menu
Special Issues
Table of Content

All issues

Online First

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

A Robustness Aware Intrusion Detection Framework Using Ensemble Learning and Imbalance Aware Preprocessing for Smart Home IoT Networks

Adi Wibowo1,2, Deris Stiawan3,*, Chonlameth Arpnikanondt4,*, Ahmad Heryanto3, Didi Rosiyadi5,*, Mohd Yazid Idris6, Fandi Kurniawan2, Tuul Triyason4, Rahmat Budiarto7
1 Department of Electrical Engineering, Faculty of Engineering, Universitas Sriwijaya, Palembang, Indonesia
2 Faculty of Engineering and Computer Science, Universitas Muhammadiyah, Kotabumi, Indonesia
3 Department of Computer Engineering, Faculty of Computer Science, Universitas Sriwijaya, Palembang, Indonesia
4 School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand
5 Research Center for Artificial Intelligence and Cybersecurity, National Research and Innovation Agency, Jakarta, Indonesia
6 Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia
7 College of Computing and Information, Al-Baha University, Alaqiq, Saudi Arabia
* Corresponding Author: Deris Stiawan. Email: email; Chonlameth Arpnikanondt. Email: email; Didi Rosiyadi. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.085248

Received 07 May 2026; Accepted 22 July 2026; Published online 20 September 2026

Abstract

The proliferation of smart home Internet of Things (IoT) devices has introduced critical security vulnerabilities that demand robust and efficient intrusion detection mechanisms. Existing machine learning-based Intrusion Detection Systems (IDS) often suffer from class imbalance bias, limited cross domain generalization, and insufficient robustness evaluation under adversarial conditions. This study proposes a robustness-aware intrusion detection framework that systematically integrates imbalance-aware preprocessing and ensemble machine learning into a unified pipeline specifically designed for resource-constrained smart home IoT environments. The framework employs Min Max normalization and Synthetic Minority Over sampling Technique (SMOTE) with strict train-test separation to prevent data leakage, operating directly on the seven packet-level traffic features available in the capture rather than on a reduced subset. Three machine learning models Decision Tree (DT), Random Forest (RF), and XGBoost are systematically compared against deep learning baselines (CNN and LSTM) across three benchmark IoT datasets: COMNETS Smart Home, IoT-23, and TON IoT. Under a conventional random-split evaluation, XGBoost achieves the highest detection performance (97.8% accuracy, 97.0% F1) at substantially lower computational cost than the deep-learning alternatives; a leakage-controlled evaluation, however, shows that much of this reflects evaluation artifacts, with the honest macro-F1 falling to 0.558 and reliable detection of the scarce benign (minority) class remaining unverified. Cross-dataset experiments are reported as a preliminary analysis; because the feature spaces could only be approximately harmonized, these results are indicative rather than conclusive evidence of cross-domain generalization. Robustness analysis under Gaussian noise injection (5%–15%) and random feature perturbation (not gradient-based adversarial attacks) reveals that ensemble models exhibit significantly lower performance degradation compared to single-tree approaches. Statistical significance testing (paired t-test, p < 0.05) validates that the observed improvements are not attributable to random variation. These results show that, once evaluation artifacts are controlled for, the honest performance of these lightweight models is modest, underscoring the importance of leakage-aware evaluation for smart-home IoT intrusion detection.

Keywords

Intrusion detection system; IoT security; ensemble learning; robustness analysis; SMOTE; smart home; cross-dataset validation

  • 57

    View

  • 15

    Download

  • 0

    Like

Related articles
Copyright© 2026 Tech Science Press
© 1997-2026 TSP (Henderson, USA) unless otherwise stated

Share Link