Abstract
With the development of artificial intelligence, neural networks have been successfully applied to the research and analysis of time series data. Considering the dynamic and spatiotemporal correlation characteristics of underwater sensor data, an anomalous data detection algorithm based on spatiotemporal correlation is proposed. First, the algorithm uses the gated recurrent unit (GRU) to learn the time series characteristics of underwater sensor data. According to the dynamics of the sensor data, the distribution of the difference between the predicted and real values is modeled by using a sliding window, and the abnormal data are preliminarily judged according to the probability density value. Then, for the spatial correlation of data, the algorithm combines its adjacent node data and uses Euclidean distance to further judge the abnormal data. Finally, the analysis of the experimental results shows that our anomalous data detection algorithm performs well at anomaly detection.
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Acknowledgement
First of all, I would like to thank my tutor Yang Qiuling. Yang Qiuling is a beautiful and very responsible teacher who has given me valuable guidance in all stages of completing this thesis. Secondly, I would like to thank all my friends, especially my three roommates, for their encouragement and support.
Funding
This project was supported by National Natural Science Foundation of China (No. 61862020) and Key Research and Development Program of Hainan Province (No. ZDYF2020199).
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Liu, N., Chen, D., Huang, H., Huang, X., Yang, Q., Xiong, N.N. (2022). Anomaly Detection of Underwater Sensor Data Based on Temporal and Spatial Correlation. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13339. Springer, Cham. https://doi.org/10.1007/978-3-031-06788-4_21
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