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Statistical monitoring of a web server for error rates: a bivariate time-series copula-based modeling approach

Author

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  • Anderson Ara
  • Francisco Louzada
  • Carlos A. R. Diniz

Abstract

The monitoring of web servers through statistical frameworks is of utmost important in order to verify possible suspicious anomalies in network traffic or misuse actions that compromise integrity, confidentiality, and availability of information. In this paper, by considering the Plackett copula function, we propose a bivariate beta-autoregressive moving average time-series model for proportion data over time, which is the case for variables present in web server monitoring such as error rates. To illustrate the proposed methodology, we monitor a Brazilian web server's rate of connection synchronization and rejection errors in a web system, with error logging rate in the past 10 min. In essence, the entire methodology may be generalized to any number of time-series of error rates.

Suggested Citation

  • Anderson Ara & Francisco Louzada & Carlos A. R. Diniz, 2017. "Statistical monitoring of a web server for error rates: a bivariate time-series copula-based modeling approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(13), pages 2287-2300, October.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:13:p:2287-2300
    DOI: 10.1080/02664763.2016.1238041
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    References listed on IDEAS

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    1. Wei Biao Wu & Zhibiao Zhao, 2007. "Inference of trends in time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(3), pages 391-410, June.
    2. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
    3. L. K. Hotta & E. C. Lucas & H. P Palaro, 2008. "Estimation of VaR Using Copula and Extreme Value Theory," Multinational Finance Journal, Multinational Finance Journal, vol. 12(3-4), pages 205-218, September.
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