Abstract
At present, the data related to the Internet of Things has shown explosive growth, and the importance of data has been greatly improved. Data collection and analysis are becoming more and more valuable. However, a large number of abnormal data will bring great trouble to our research, and even lead people into misunderstandings. Therefore, anomaly detection is particularly necessary and important. The purpose of this paper is to find an efficient and accurate outlier detection algorithm. Our work also analyzes their advantages and disadvantages theoretically. At the same time, the effects of the data set size, number of proximity points, and data dimension on CPU time and precision are discussed. The performance, advantages and disadvantages of each algorithm in dealing with high-dimensional data are compared and analyzed. Finally, the algorithm is used to analyze the actual anomaly data collected from the Internet of Things and analyze the results. The results show that the LOF algorithm can find the abnormal data in the data set in a shorter time and with higher accuracy.
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Acknowledgement
This work is partially supported by Project No. 5211DS16001R of State Grid Zhejiang Electric Power Co., Ltd. This work is also supported by the National Natural Science Foundation of China (NSFC) under Grant No. 61502374.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wang, Z., Liu, Z., Yuan, X., Xu, Y., Li, R. (2019). Assessing Data Anomaly Detection Algorithms in Power Internet of Things. In: Gao, H., Wang, X., Yin, Y., Iqbal, M. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 268. Springer, Cham. https://doi.org/10.1007/978-3-030-12981-1_28
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DOI: https://doi.org/10.1007/978-3-030-12981-1_28
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