Abstract
In cyber physical systems (CPS), anomaly detection is an important means to ensure the quality of sensory data and the effect of data fusion. However, the challenge of detecting anomalies in data stream has become harder over time due to its large scale, multi-dimension and spatiotemporal features. In this paper, a novel anomaly detection algorithm for spatiotemporal data is proposed. The algorithm firstly uses data mining technology to dig out correlation rules between multidimensional data attributes, and output the strong association attributes set. Then the corresponding specific association rules for data anomaly detection are built based on machine learning method. Experimental results show that the algorithm is superior to other algorithms.
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References
Cheng S, Cai Z, Li J, Fang X (2015) Drawing dominant dataset from big sensory data in wireless sensor networks. In: The 34th annual IEEE international conference on computer communications (INFOCOM 2015), pp 531–539
Cheng S, Cai Z, Li J, Gao H (2017) Extracting kernel dataset from big sensory data in wireless sensor networks. IEEE Trans Knowl Data Eng 29(4):813–827
Cheng S, Cai Z, Li J (2015) Curve query processing in wireless sensor networks. IEEE Trans Veh Technol 64(11):5198–5209
He Z, Cai Z, Cheng S, Wang X (2015) Approximate aggregation for tracking quantiles and range countings in wireless sensor networks. Theoret Comput Sci 607(3):381–390
Shahid Nauman, Naqvi Ijaz Haider, Qaisar Saad Bin (2015) Characteristics and classification of outlier detection techniques for wireless sensor networks in harsh environments: a survey. Artif Intell Rev 43(2):193–228
Koh JLY, Lee ML, Hsu W, Kai TL (2007) Correlation-based detection of attribute outliers. Int Conf Database Syst Adv Appl 4443:164–175
Wang J, Xu Z (2016) Spatio-temporal texture modelling for real-time crowd anomaly detection. Comput Vis Image Underst 144(C):177–187
Akoglu L, Tong H, Koutra D (2015) Graph based anomaly detection and description: a survey. Data Min Knowl Disc 29(3):626–688
Khaitan SK, Mccalley JD (2015) Design techniques and applications of cyberphysical systems: a survey. IEEE Syst J 9(2):350–365
Zhenfg X, Cai Z, Yu J, Wang C, Li Y (2017) Follow but no track: privacy preserved profile publishing in cyber-physical social systems. IEEE Internet Things J 4(6):1868–1878
Ghorbel O, Ayadi A, Loukil K, Bensaleh MS, Abid M (2017) Classification data using outlier detection method in Wireless sensor networks. In: IEEE wireless communications and mobile computing conference, pp 699–704
Acknowledgments
This work is supported by the Science and Technology Department of Sichuan Province (Grant no. 2017HH0075, 2016GZ0075, 2017JZ0031).
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Chen, A., Chen, Y., Lu, G., Zhang, L., Luo, J. (2019). An Anomaly Detection Algorithm for Spatiotemporal Data Based on Attribute Correlation. In: Park, J., Loia, V., Choo, KK., Yi, G. (eds) Advanced Multimedia and Ubiquitous Engineering. MUE FutureTech 2018 2018. Lecture Notes in Electrical Engineering, vol 518. Springer, Singapore. https://doi.org/10.1007/978-981-13-1328-8_11
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DOI: https://doi.org/10.1007/978-981-13-1328-8_11
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