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Journal of Sensors - Volume 2017 2017, Article ID 8782131, 15 pages -

Research ArticleInstitute of Telecommunications, Faculty of Telecommunications and Electrical Engineering, University of Technology and Life Sciences UTP, Ul. Kaliskiego 7, 85-789 Bydgoszcz, Poland

Correspondence should be addressed to Tomasz Andrysiak

Received 10 March 2017; Revised 31 May 2017; Accepted 13 June 2017; Published 18 July 2017

Academic Editor: José R. Villar

Copyright © 2017 Tomasz Andrysiak et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The article presents solutions to anomaly detection in network traffic for critical smart metering infrastructure, realized with the use of radio sensory network. The structure of the examined smart meter network and the key security aspects which have influence on the correct performance of an advanced metering infrastructure possibility of passive and active cyberattacks are described. An effective and quick anomaly detection method is proposed. At its initial stage, Cook’s distance was used for detection and elimination of outlier observations. So prepared data was used to estimate standard statistical models based on exponential smoothing, that is, Brown’s, Holt’s, and Winters’ models. To estimate possible fluctuations in forecasts of the implemented models, properly parameterized Bollinger Bands was used. Next, statistical relations between the estimated traffic model and its real variability were examined to detect abnormal behavior, which could indicate a cyberattack attempt. An update procedure of standard models in case there were significant real network traffic fluctuations was also proposed. The choice of optimal parameter values of statistical models was realized as forecast error minimization. The results confirmed efficiency of the presented method and accuracy of choice of the proper statistical model for the analyzed time series.

Autor: Tomasz Andrysiak, Łukasz Saganowski, and Piotr Kiedrowski



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