Automatic Threat Detection in ICT Systems by Selected Data Mining Methods and Software

Authors

  • Kamil Sapała Free Construction Sp. z o.o.
  • Marcin Piołun-Noyszewski Free Construction Sp. z o.o.
  • Marcin Weiss Free Construction Sp. z o.o.

DOI:

https://doi.org/10.18778/0208-6018.337.03

Keywords:

ICT systems, electronic documents, transformations, data mining methods

Abstract

The paper presents some real‑time analytical solutions that work in a proprietary‑designed system for IT security. It describes automatic methods of data transformations and analysis aiming at detection of potential threats (irregular system events, abnormal user behavior) both for time series and text documents without human supervision. Automation procedures used for time series and text documents are presented. Analyzed data was collected by Free Construction while protecting systems of electronic documents repositories (also including the Enterprise Content Management standards).

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References

Cichowicz T., Frankiewicz M., Rytwiński F., Wasilewski J., Zakrzewicz M. (2012), Anomaly Detection in Time Series for System Monitoring, „The Poznan School of Banking Research”, nr 40, s. 115–130.
Google Scholar

Friedman N., Geiger D., Goldszmidt M. (1997), Bayesian network classifiers, „Machine Learning”, t. 29(2–3), s. 131–163.
Google Scholar

Harvey A.C. (1990), Forecasting, structural time series models and the Kalman filter, Cambridge University Press, New York.
Google Scholar

Hyndman R.J., Khandakar Y. (2007), Automatic time series for forecasting: the forecast package for R. Working paper 06/07, Monash University, Department of Econometrics and Business Statistics, Melbourne.
Google Scholar

Lula P. (2005), Text mining jako narzędzie pozyskiwania informacji z dokumentów tekstowych, Stat-Soft Polska, https://media.statsoft.pl/_old_dnn/downloads/text_mining_jako_narzedzie_pozyskiwania.pdf [dostęp: 22.11.2016].
Google Scholar

Lula P., Wójcik K., Tuchowski J. (2016), Feature‑based sentiment analysis of opinions in polish, „Research Papers of Wrocław University of Economics: Taxonomy 27. Classification and Data Analysis. Theory and Applications”, s. 153–164.
Google Scholar

Mirończuk M. (2012), Review of methods and text data mining, „Studies and Materials in Applied Computer Science”, t. 4, nr 6, s. 25–42.
Google Scholar

Mitrea C.A., Lee C.K.M., Wu Z. (2009), A comparison between neural networks and traditional forecasting methods: A case study, „International Journal of Engineering Business Management”, t. 1, s. 19–24.
Google Scholar

Okasha M.K., Yaseen A.A. (2013), Comparison between ARIMA models and artificial neural networks in forecasting Al‑Quds indices of Palestine stock exchange market, The 25th Annual International Conference on Statistics and Modeling in Human and Social Sciences, Departmentof Statistics, Faculty of Economics and Political Science, Cairo University, Cairo.
Google Scholar

Sapała K., Piołun‑Noyszewski M., Weiss M. (2017), Porównanie wybranych metod statystycznych i metod sztucznej inteligencji do przewidywania zdarzeń w oprogramowaniu zabezpieczającym systemy przechowywania dokumentów cyfrowych, w tym systemy klasy Enterprise Content Management, „Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu. Taksonomia 29. Klasyfikacja i analiza danych: teoria i zastosowania”, s. 159–166.
Google Scholar

Zhang G.P. (2003), Time series forecasting using a hybrid ARIMA and neural network model, „Neurocomputing”, t. 50, s. 159–175.
Google Scholar

Published

2018-09-20

How to Cite

Sapała, K., Piołun-Noyszewski, M., & Weiss, M. (2018). Automatic Threat Detection in ICT Systems by Selected Data Mining Methods and Software. Acta Universitatis Lodziensis. Folia Oeconomica, 4(337), 39–52. https://doi.org/10.18778/0208-6018.337.03

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