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Protection Against Information in eSociety: Using Data Mining Methods to Counteract Unwanted and Malicious Data

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Digital Transformation and Global Society (DTGS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 745))

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Abstract

Despite the positive aspects of usage of the Internet and social networks within the concept of eSociety, huge data collections available for viewing and analysis to the user of the Internet can contain information which can be unwanted or malicious. The paper considers the problem of protection of users in the “electronic society” infrastructure against such information. The paper discusses the nature of the problem and possible approaches to its solution. To solve the problem it is proposed to use modular approach to construction of automated systems of protection against information, based on application of Data Mining methods. We consider the implementation of the system of protection against unwanted and harmful content, based on the classifier with three-level hierarchical architecture. Its experimental evaluation, which confirmed high efficiency of functioning of the system for most of the analyzed categories of web sites, are also discussed.

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Notes

  1. 1.

    http://urlblacklist.com.

  2. 2.

    http://www.shallalist.de.

  3. 3.

    https://www.dmoz.org.

  4. 4.

    https://rapidminer.com.

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Acknowledgements

This research is being supported by the grant of RSF#15-11-30029 in SPIIRAS.

Author information

Authors and Affiliations

  1. St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, 39, 14 Liniya, Saint Petersburg, Russia

    Igor Kotenko, Igor Saenko & Andrey Chechulin

  2. St. Petersburg National Research University of Information Technologies, Mechanics and Optics, 49, Kronverkskiy prospekt, Saint-Petersburg, Russia

    Igor Kotenko, Igor Saenko & Andrey Chechulin

Authors
  1. Igor Kotenko
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  2. Igor Saenko
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  3. Andrey Chechulin
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Corresponding author

Correspondence to Igor Kotenko .

Editor information

Editors and Affiliations

  1. National Research University Higher School of Economics, St. Petersburg, Russia

    Daniel A. Alexandrov

  2. ITMO University, St. Petersburg, Russia

    Alexander V. Boukhanovsky

  3. ITMO University, St. Petersburg, Russia

    Andrei V. Chugunov

  4. National Research University Higher School of Economics, St. Petersburg, Russia

    Yury Kabanov

  5. National Research University Higher School of Economics, St. Petersburg, Russia

    Olessia Koltsova

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Kotenko, I., Saenko, I., Chechulin, A. (2017). Protection Against Information in eSociety: Using Data Mining Methods to Counteract Unwanted and Malicious Data. In: Alexandrov, D., Boukhanovsky, A., Chugunov, A., Kabanov, Y., Koltsova, O. (eds) Digital Transformation and Global Society. DTGS 2017. Communications in Computer and Information Science, vol 745. Springer, Cham. https://doi.org/10.1007/978-3-319-69784-0_15

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  • DOI: https://doi.org/10.1007/978-3-319-69784-0_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69783-3

  • Online ISBN: 978-3-319-69784-0

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