Hybrid Machine Learning Model for Efficient Malware Network Attack Detection in IoT Environment

Bommireddy Srivani, Ankalugari Niharika, Machapura Sailaja, Manini Ramyasri, Pochamireddy Tejasree

2025

Abstract

The exponential growth of the Internet of Things (IoT) has significantly increased the attack surface for cyber threats, making malware-based network attacks a critical security challenge. Traditional intrusion detection systems (IDS) often struggle to cope with the high volume, complexity, and evolving nature of these attacks. To address this, we propose a Hybrid Machine Learning Model that integrates supervised learning, ensemble techniques, and deep learning-based anomaly detection to enhance the accuracy and efficiency of malware detection in IoT networks. The proposed model leverages feature selection, real-time traffic analysis, and hybrid classification to detect malicious network activities while minimizing false positives. We employ a combination of Decision Tree, Random Forest, and Deep Neural Networks (DNNs) to classify benign and malicious traffic with high precision. Experimental evaluations using benchmark datasets demonstrate that our model outperforms traditional IDS models, achieving superior detection rates, lower latency, and enhanced robustness against sophisticated cyberattacks. Despite its high efficiency, challenges such as adversarial attacks, scalability concerns, and real-time deployment overhead remain open areas for further research. Future work will explore federated learning, blockchain-based authentication, and explainable AI (XAI) to further strengthen IoT security. The proposed hybrid approach provides a scalable, intelligent, and real-time malware detection system, contributing to a more resilient IoT security framework.

Paper Citation

in Harvard Style

Srivani B., Niharika A., Sailaja M., Ramyasri M. and Tejasree P. (2025). Hybrid Machine Learning Model for Efficient Malware Network Attack Detection in IoT Environment. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 770-774. DOI: 10.5220/0013905300004919

in Bibtex Style

@conference{icrdicct`2525,
author={Bommireddy Srivani and Ankalugari Niharika and Machapura Sailaja and Manini Ramyasri and Pochamireddy Tejasree},
title={Hybrid Machine Learning Model for Efficient Malware Network Attack Detection in IoT Environment},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={770-774},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013905300004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Hybrid Machine Learning Model for Efficient Malware Network Attack Detection in IoT Environment
SN - 978-989-758-777-1
AU - Srivani B.
AU - Niharika A.
AU - Sailaja M.
AU - Ramyasri M.
AU - Tejasree P.
PY - 2025
SP - 770
EP - 774
DO - 10.5220/0013905300004919
PB - SciTePress