Abstract
With the wide application of underwater wireless sensor network, underwater node positioning technology also plays an important role in underwater wireless sensor network. However, nodes are very easy to be captured as malicious nodes. In order to deal with the problem of node security in a more comprehensive way, a node security situation assessment technology based on BP neural network is proposed to better identify malicious nodes. Through the training of historical interaction data between nodes, a prediction model is obtained, and the reliability of nodes is determined according to the calculated situation value in the assessment system. The algorithm proposed in this paper can evaluate the security situation of nodes more comprehensively, identify malicious nodes more accurately, and maintain the security of nodes.
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Acknowledgment
This work was supported by the following projects: the National Natural Science Foundation of China (61862020); the key research and development project of Hainan Province (ZDYF2018006); Hainan University-Tianjin University Collaborative Innovation Foundation Project (HDTDU202005).
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Huang, H., Liang, K., Huang, X., Yang, Q. (2021). Safety Situation Assessment of Underwater Nodes Based on BP Neural Network. In: MacIntyre, J., Zhao, J., Ma, X. (eds) The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIOT 2020. Advances in Intelligent Systems and Computing, vol 1282. Springer, Cham. https://doi.org/10.1007/978-3-030-62743-0_32
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DOI: https://doi.org/10.1007/978-3-030-62743-0_32
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