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Intrusion Detection and Risk Evaluation in Online Transactions Using Partitioning Methods

  • Hossein Yazdani
  • Kazimierz ChorośEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 833)

Abstract

Security is the main issue for real time systems, specially for financial and banking systems. Some of the customers who pay much attention to confidentiality and security on their network activities and transactions prefer to use the most secure channels, and for the others speed and the ease of services are more important. An optimized method should be a solution, but both strategies follow one common idea that any anomaly, abnormality, and intrusion should be handled in advance, as the reputation of each organization is based on trust. This paper proposes a new method with the aim of considering any anomaly in advance, in addition to partitioning strategy. The BFPM method makes use of the well-known Fuzzy C-Means clustering algorithm to evaluate whether packets or transactions are risky or not, and in what extent they will be risky in the near future. The proposed method aims to provide a flexible search space to cover prevention and prediction techniques at the same time.

Keywords

Network security Intrusion detection Online transaction Partitioning methods Fuzzy C-Means algorithm BFPM method Clustering Risk evaluation Object movement 

References

  1. 1.
    Edge, K., Raines, R., Grimaila, M., Baldwin, R., Bennington, R., Reuter, C.: The use of attack and protection trees to analyze security for an online banking system. In: Proceedings of the Annual Hawaii International Conference on System Sciences, p. 144b. IEEE (2007)Google Scholar
  2. 2.
    Chio, C., Freeman, D.: Machine Learning and Security. O’Reilly (2017)Google Scholar
  3. 3.
    Buczak, A.L., Guven, E.: A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tutorials 18(2), 1153–1176 (2016)CrossRefGoogle Scholar
  4. 4.
    Hoppner, F.: Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition. Wiley (1999)Google Scholar
  5. 5.
    Cannon, R.L., Dave, J.V., Bazdek, J.C.: Efficient implementation of the fuzzy c-means clustering algorithms. IEEE Trans. Patt. Anal. Mach. Intell. PAMI–8(2), 248–255 (1986)CrossRefGoogle Scholar
  6. 6.
    Anderson, D.T., Bezdek, J.C., Popescu, M., Keller, J.M.: Comparing fuzzy, probabilistic, and possibilistic partitions. IEEE Trans. Fuzzy Syst. 18(5), 906–918 (2010)CrossRefGoogle Scholar
  7. 7.
    Yazdani, H.: Fuzzy possibilistic on different search spaces. In: Proceedings of the International Symposium on Computational Intelligence and Informatics, pp. 283–288. IEEE (2016)Google Scholar
  8. 8.
    Cao, B., Fan, Q.: The infrastructure and security management of mobile banking system. In: IEEE International Conference on E-Service and E-Entertainment, pp. 1–3 (2010)Google Scholar
  9. 9.
    Paliwal, S., Gupta, R.: Denial-of-Service, probing and remote to user (R2L) attack detection using genetic algorithm. Int. J. Comput. Appl. 60(19), 57–62 (2012)Google Scholar
  10. 10.
    Shon, T., Moon, J.: A hybrid machine learning approach to network anomaly detection. J. Inf. Sci. 177(18), 3799–3821 (2007)CrossRefGoogle Scholar
  11. 11.
    Tsai, C.F., Hsu, Y.F., Lin, C.Y., Lin, W.Y.: Intrusion detection by machine learning: a review. Expert Syst. Appl. 36(10), 11994–12000 (2009)CrossRefGoogle Scholar
  12. 12.
    Ahmed, M., Mahmood, A., Hu, J.: A survey of network anomaly detection techniques. J. Netw. Comput. Appl. 60, 19–31 (2016)CrossRefGoogle Scholar
  13. 13.
    Aamir, R., Ashfaq, R., Wang, X.Z., Huang, J.Z., Abbas, H., He, Y.L.: Fuzziness based semi-supervised learning approach for intrusion detection system. J. Inf. Sci. 378, 484–497 (2017)CrossRefGoogle Scholar
  14. 14.
    Zhou, J., Chen, C.L.P., Chen, L., Li, H.X.: A collaborative fuzzy clustering algorithm in distributed network environments. IEEE Trans. Fuzzy Syst. 22(6), 1443–1456 (2014)CrossRefGoogle Scholar
  15. 15.
    Masduki, B.W., Ramli, K., Saputra, F.A., Sugiarto, D.: Study on implementation of machine learning methods combination for improving attacks detection accuracy on intrusion detection systems (IDS). In: International Conference on Quality in Research, pp. 56–64. IEEE (2015)Google Scholar
  16. 16.
    Jeya, P.G., Ravichandran, M., Ravichandran, C.S.: Efficient classifier for R2L and U2R attacks. Int. J. Comput. Appl. 45(21), 28–32 (2012)Google Scholar
  17. 17.
    Kiljan, S., Eekelen, M.V., Vranken, H.: Towards a virtual bank for evaluating security aspects with focus on user behavior. In: SAI Computing Conference, pp. 1068–1075. IEEE (2016)Google Scholar
  18. 18.
    Yazdani, H., Ortiz-Arroyo, D., Choroś, K., Kwaśnicka, H.: Applying bounded fuzzy possibilistic method on critical objects. In: Proceedings of the International Symposium on Computational Intelligence and Informatics, pp. 271–276. IEEE (2016)Google Scholar
  19. 19.
    Yazdani, H., Kwaśnicka, H.: Fuzzy classification method in credit risk. In: Proceedings of the International Conference on Computational Collective Intelligence. Lecture Notes in Computer Science, vol. 7653, pp. 495–505. Springer (2012)Google Scholar
  20. 20.
    Yazdani, H., Ortiz-Arroyo, D., Choroś, K., Kwaśnicka, H.: On high dimensional searching space and learning methods. In: Data Science and Big Data: An Environment of Computational Intelligence, pp. 29–48. Springer (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Computer Science and ManagementWrocław University of Science and TechnologyWrocławPoland

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