Real-Time Anomaly Detection in IoT Networks Using Deep Learning over Wireless Channels

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 3

Year of Publication : 2024

Authors : K. Sudharson, C.S. Anita, M.A. Berlin, G. Eswari @ Petchiammal, J. Deepika, K. Selvi

10.14445/23488379/IJEEE-V11I3P128

How to Cite?

K. Sudharson, C.S. Anita, M.A. Berlin, G. Eswari @ Petchiammal, J. Deepika, K. Selvi, "Real-Time Anomaly Detection in IoT Networks Using Deep Learning over Wireless Channels," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 3, pp. 332-340, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I3P128

Abstract:

This study introduces IoT-AnomalyNet, a novel deep-learning approach designed for real-time anomaly detection in IoT networks operating over wireless channels. IoT-AnomalyNet combines long short-term memory networks (LSTMs), Convolutional Neural Networks (CNNs), autoencoders, attention mechanisms, and hybrid architectures to effectively identify patterns in both spatial and temporal dimensions within IoT sensor data streams. Through comprehensive experimentation with diverse datasets and IoT sensor readings, IoT-AnomalyNet achieves an impressive accuracy rate of 95.73% for anomaly detection. Notably, IoT-AnomalyNet outperforms traditional machine learning methods with remarkable recall (97.5%) and precision (95.5%) rates for normal instances and recall (97.85%) and precision (96.24%) rates for attack instances. These results underscore the efficacy of deep learning methodologies in accurately detecting anomalies in real-time IoT data streams transmitted via wireless networks. By proactively identifying abnormal behaviors, IoT-AnomalyNet holds significant promise in mitigating risks, ensuring continuous operation, and enhancing the security and reliability of IoT systems.

Keywords:

Anomaly detection, Attention mechanisms, Deep Learning, IoT networks, Wireless channels.

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