Clustering Algorithms and Data Streams for Supervised Control of Data and Prevention of Threats in Mobile Application Systems

  • Aneta MajchrzyckaEmail author
  • Aneta Poniszewska-Marańda
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 521)


The paper aims to present the possibilities of application of various data mining techniques in order to increase the security level of mobile application systems. The scope of work incorporates the usage of clustering algorithms—particularly Density-Based Spatial Clustering of Applications with Noise (DBScan)—as well as other mechanisms connected with data streams. The proposed solution is based on the process of monitoring the incoming server requests obtained from mobile devices which use server application to connect to the data.


Mobile systems Security of mobile applications Data exploration Data mining Clustering Data streams 


  1. 1.
    Tandon, G.: Machine Learning for Host-based Anomaly Detection. Florida Institute of Technology, Florida (2008)Google Scholar
  2. 2.
    Aggarwal, C.C., Yu, P.S.: Privacy-Preserving Data Mining: Models and Algorithms, Advances in Database Systems. Springer, Switzerland (2008)Google Scholar
  3. 3.
    Aggarwal, C.C.: Data Streams: Models and Algorithms, Advances in Database Systems. IBM T.J. Watson Research Centre, Springer, Switzerland (2007)Google Scholar
  4. 4.
    Markov, Z., Larose, D.T.: Data Mining the Web: Uncovering Patterns in Web Content, Structure and Usage. Wiley, New Jersey (2007)CrossRefzbMATHGoogle Scholar
  5. 5.
    Maloof, M.A.: Machine Learning and Data Mining for Computer: Methods and Applications, Advanced Information and Knowledge Processing. Springer, Switzerland (2006)Google Scholar
  6. 6.
    Dua, S., Du. X.: Data Mining and Machine Learning in Cybersecurity. CRC Press, Auerbach Publications, Taylor & Francis Group, LLC, Boca Raton (2011)Google Scholar
  7. 7.
    Witten, I.H., Frank, E., Hall M.A.: Data Mining: Practical Machine Learning Tools and Techniques. 3rd edn. Morgan Kaufamn, Burlington (2011)Google Scholar
  8. 8.
    Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and issues in data stream systems. In: Proceedings of 21st ACM SIGMOD-SIGACT-SIGART, pp. 1–16 (2002)Google Scholar
  9. 9.
    Michalska, A., Poniszewska-Marańda, A.: Security risks and their prevention capabilities in mobile application development. Inf. Syst. Manag. 4(2), 123–134 (2015) (WULS Press)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Information TechnologyLodz University of TechnologyŁódźPoland

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