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A Deep Learning Based Anomaly Detection Model for IoT Networks

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Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology (IoTCIT 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1197))

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Abstract

Internet of Things (IoT) has demonstrated tremendous advantages in various industry and research fields. The IoT device number rapidly increased, followed by more serious safety hazard. These anomalies can influence the performance of system or even worse destroy the function of entire system. Anomaly detection methods are investigated to identify unusual states or malicious behaviors. This paper proposes a deep learning-based anomaly detection model to detect and classify anomalies in IoT. The proposed model is based on Residual Networks and Bi-directional GRU, which can fully utilize the spatial and temporal features of network traffic data. Moreover, attention mechanism is utilized to extract key features to improve the classification performance of the model. Experimental results show that the proposed model has better detection and classification performance.

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Acknowledgement

The work is supported by NSFC project 61601264 and Shandong Province Statistical Research Project KT23079.

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Authors and Affiliations

  1. ShanDong JiaoTong University, HaiTang Road 5001, Jinan, 250357, China

    Li E. Dai, Xiao Wang & Shuo Bo Xu

Authors
  1. Li E. Dai
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  2. Xiao Wang
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  3. Shuo Bo Xu
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Corresponding author

Correspondence to Shuo Bo Xu .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, Hunan, China

    Jian Dong

  2. School of Information Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Long Zhang

  3. School of Information and Control Engineering, China University of Mining and Technolog, Xuzhou, Jiangsu, China

    Deqiang Cheng

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Dai, L.E., Wang, X., Xu, S.B. (2024). A Deep Learning Based Anomaly Detection Model for IoT Networks. In: Dong, J., Zhang, L., Cheng, D. (eds) Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology. IoTCIT 2023. Lecture Notes in Electrical Engineering, vol 1197. Springer, Singapore. https://doi.org/10.1007/978-981-97-2757-5_20

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  • DOI: https://doi.org/10.1007/978-981-97-2757-5_20

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2756-8

  • Online ISBN: 978-981-97-2757-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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