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Internet of Things Intrusion Detection System Based on Convolutional Neural Network

Jie Yin1,2,3,*, Yuxuan Shi1, Wen Deng1, Chang Yin1, Tiannan Wang1, Yuchen Song1, Tianyao Li1, Yicheng Li1

1 Department of Computer Information and Cyber Security, Jiangsu Police Institute, Nanjing, China
2 Engineering Research Center of Electronic Data Forensics Analysis, Nanjing, China
3 Key Laboratory of Digital Forensics, Department of Public Security of Jiangsu Province, Nanjing, China

* Corresponding Author: Jie Yin. Email: email

Computers, Materials & Continua 2023, 75(1), 2119-2135. https://doi.org/10.32604/cmc.2023.035077

Abstract

In recent years, the Internet of Things (IoT) technology has developed by leaps and bounds. However, the large and heterogeneous network structure of IoT brings high management costs. In particular, the low cost of IoT devices exposes them to more serious security concerns. First, a convolutional neural network intrusion detection system for IoT devices is proposed. After cleaning and preprocessing the NSL-KDD dataset, this paper uses feature engineering methods to select appropriate features. Then, based on the combination of DCNN and machine learning, this paper designs a cloud-based loss function, which adopts a regularization method to prevent overfitting. The model consists of one input layer, two convolutional layers, two pooling layers and three fully connected layers and one output layer. Finally, a framework that can fully consider the user’s privacy protection is proposed. The framework can only exchange model parameters or intermediate results without exchanging local individuals or sample data. This paper further builds a global model based on virtual fusion data, so as to achieve a balance between data privacy protection and data sharing computing. The performance indicators such as accuracy, precision, recall, F1 score, and AUC of the model are verified by simulation. The results show that the model is helpful in solving the problem that the IoT intrusion detection system cannot achieve high precision and low cost at the same time.

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APA Style
Yin, J., Shi, Y., Deng, W., Yin, C., Wang, T. et al. (2023). Internet of things intrusion detection system based on convolutional neural network. Computers, Materials & Continua, 75(1), 2119-2135. https://doi.org/10.32604/cmc.2023.035077
Vancouver Style
Yin J, Shi Y, Deng W, Yin C, Wang T, Song Y, et al. Internet of things intrusion detection system based on convolutional neural network. Comput Mater Contin. 2023;75(1):2119-2135 https://doi.org/10.32604/cmc.2023.035077
IEEE Style
J. Yin et al., "Internet of Things Intrusion Detection System Based on Convolutional Neural Network," Comput. Mater. Contin., vol. 75, no. 1, pp. 2119-2135. 2023. https://doi.org/10.32604/cmc.2023.035077



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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