Abstract
Smart home depending on Internet of things (IoT) technologies is facing severe risks in information security. An intrusion detection method fused deep learning and fuzzy neural network for smart home is proposed, according to the method, the data features are built by deep learning methods, the high-dimensional data is mapped into low-dimensional one, and the categories of attack can be analyzed and distinguished based on fuzzy neural network. A method used in optimizing network depth also is provided, and this can overcome the problem from the traditional method which determines the layer number of network depending on experience. The simulation results show that the proposed method including artificial intelligence can improve the detection rate of attacks, for example, the detection rate can reach 94% for the denial of service attack and remote illegal access, and the detection rate of the tested new types of attacks in the network exceeds 60%.
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Hu, X., Zhang, Q., Yang, X., Yang, L. (2022). An Intrusion Detection Method Fused Deep Learning and Fuzzy Neural Network for Smart Home. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13393. Springer, Cham. https://doi.org/10.1007/978-3-031-13870-6_52
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DOI: https://doi.org/10.1007/978-3-031-13870-6_52
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