Anomaly detection in Internet of Things equipment traffic with the approach of combining deep learning models

Yadegari, Vahid; Sohrab, Samad; Mahmoudian, Vahid

Anomaly detection in Internet of Things equipment traffic with the approach of combining deep learning models

Document Type : Original Article

Authors

¹ PhD student, Allameh Tabatabai University, Tehran, Iran

² Researcher, Qom Higher Education Institute, Qom, Iran

³ Ph.D. student, Faculty of Computer Science, Islamic Azad University, North Tehran Branch, Tehran, Iran

Abstract

Network traffic classifiers play a critical role in network monitoring systems by detecting anomalies in network flows based on communication features. This is particularly significant for managing and monitoring Internet of Things (IoT) networks. In this study, a novel method for network traffic classification is proposed, leveraging a combination of deep learning models tailored for IoT traffic. Experimental results demonstrate that the hybrid CNN+RNN-2 model achieves an accuracy of 83.58%, outperforming standalone and other hybrid models. By combining local feature extraction through Convolutional Neural Networks (CNN) and temporal dependency analysis via Recurrent Neural Networks (RNN), the proposed model effectively identifies complex patterns and improves detection accuracy. Furthermore, the results indicate that the CNN+RNN-2 model surpasses traditional supervised, semi-supervised, and unsupervised methods without requiring manual feature engineering. The use of deep learning, with its capability for automatic feature extraction and learning complex patterns, provides a significant advantage over conventional artificial intelligence techniques.

Keywords

20.1001.1.23224347.1404.13.2.5.2

Main Subjects

Vulnerabilities and threats of cyber space

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Anomaly detection in Internet of Things equipment traffic with the approach of combining deep learning models

References

Volume 13, Issue 2 - Serial Number 50
Summer
July 2025
Pages 45-55

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Anomaly detection in Internet of Things equipment traffic with the approach of combining deep learning models

References

Volume 13, Issue 2 - Serial Number 50SummerJuly 2025Pages 45-55

Files

History

Share

How to cite

Statistics

Volume 13, Issue 2 - Serial Number 50
Summer
July 2025
Pages 45-55