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2018 | OriginalPaper | Chapter

A Novel Hybrid Data Reduction Strategy and Its Application to Intrusion Detection

Authors : Vitali Herrera-Semenets, Osvaldo Andrés Pérez-García, Andrés Gago-Alonso, Raudel Hernández-León

Published in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Publisher: Springer International Publishing

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Abstract

The presence of useless information and the huge amount of data generated by telecommunication services can affect the efficiency of traditional Intrusion Detection Systems (IDSs). This fact encourage the development of data preprocessing strategies for improving the efficiency of IDSs. On the other hand, improving such efficiency relying on the data reduction strategies, without affecting the quality of the reduced dataset (i.e. keeping the accuracy during the classification process), represents a challenge. Also, the runtime of commonly used strategies is usually high. In this paper, a novel hybrid data reduction strategy is presented. The proposed strategy reduces the number of features and instances in the training collection without greatly affecting the quality of the reduced dataset. In addition, it improves the efficiency of the classification process. Finally, our proposal is favorably compared with other hybrid data reduction strategies.

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Zuech, R., Khoshgoftaar, T.M., Wald, R.: Intrusion detection and big heterogeneous data: a survey. J. Big Data 2(1), 1–41 (2015)CrossRef

Cortes, C., Pregibon, D.: Signature-based methods for data streams. Data Mining Knowl. Discov. 5(3), 167–182 (2001)CrossRefMATH

Kotsiantis, S.B.: Supervised machine learning: a review of classification techniques. In: Proceedings of the 2007 Conference on Emerging Artificial Intelligence Applications in Computer Engineering, Amsterdam, The Netherlands, pp. 3–24 (2007)

Ghahramani, Z.: Unsupervised learning. In: Bousquet, O., von Luxburg, U., Rätsch, G. (eds.) ML -2003. LNCS (LNAI), vol. 3176, pp. 72–112. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28650-9_5 CrossRef

Aggarwal, C.C.: Data Mining: The Textbook. Springer, Heidelberg (2015)CrossRefMATH

Chen, T., Zhang, X., Jin, S., Kim, O.: Efficient classification using parallel and scalable compressed model and its application on intrusion detection. Expert Syst. Appl. 41(13), 5972–5983 (2014)CrossRef

Olvera-Lopez, J.A., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Kittler, J.: A review of instance selection methods. Artif. Intell. Rev. 34(2), 133–143 (2010)CrossRef

Chou, C.H., Kuo, B.H. and Chang, F.: The generalized condensed nearest neighbor rule as a data reduction method. In: 18th International Conference on Pattern Recognition (ICPR 2006), vol. 2, pp. 556–559 (2006)

Wilson, D.R., Martínez, T.R.: Reduction techniques for instance-based learning algorithms. Mach. Learn. 38(3), 257–286 (2000)CrossRefMATH

10.

Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Mach. Learn. 11(1), 63–90 (1993)CrossRefMATH

11.

Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)MathSciNetCrossRefMATH

12.

Liu, W., Liu, S., Gu, Q., Chen, J., Chen, X., Chen, D.: Empirical studies of a two-stage data preprocessing approach for software fault prediction. IEEE Trans. Reliab. 65(1), 38–53 (2016)CrossRef

13.

KDDCup 1999: Computer network intrusion detection. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html. Accessed 25 Feb 2017

14.

Song, J.: CDMC2013 intrusion detection dataset. Department of Science & Technology Security, Korea Institute of Science and Technology Information (KISTI) (2013)

15.

Horng, S.J., Su, M.Y., Chen, Y.H., Kao, T.W., Chen, R.J., Lai, J.L., Perkasa, C.D.: A novel intrusion detection system based on hierarchical clustering and support vector machines. Expert Syst. Appl. 38(1), 306–313 (2011)CrossRef

16.

Aburomman, A.A., Reaz, M.B.I.: A novel SVM-kNN-PSO ensemble method for intrusion detection system. Appl. Soft Comput. 38, 360–372 (2016)CrossRef

Title: A Novel Hybrid Data Reduction Strategy and Its Application to Intrusion Detection
Authors: Vitali Herrera-Semenets
Osvaldo Andrés Pérez-García
Andrés Gago-Alonso
Raudel Hernández-León
Publisher: Springer International Publishing
Book: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Print ISBN: 978-3-319-75192-4

Electronic ISBN: 978-3-319-75193-1

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-75193-1_35

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