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One-Class Classification of Low Volume DoS Attacks with Genetic Programming

  • Stjepan PicekEmail author
  • Erik Hemberg
  • Domagoj Jakobovic
  • Una-May O’Reilly
Conference paper
Part of the Genetic and Evolutionary Computation book series (GEVO)

Abstract

We use Genetic Programming in a machine learning approach to learn a detector of DoS-related network intrusion events. We present a one class classifier technique that trains a model from one class of data—normal, i.e., non-intrusion events. Our technique, after ensemble fusion, is competitive with one-class modeling with Support Vector Machines. We compare with three datasets and our best GP-based classifiers are able to outperform one-class SVM. For two out of four test cases, the advantage of GP classifiers when compared with one-class SVM is less than 1% which does not represent a significant improvement. On the last two cases, GP achieves significantly better results and making it a viable choice for anomaly detection task.

Notes

Acknowledgements

This work has been supported in part by Cybersecurity@CSAIL initiative. Additionally, this work has been supported in part by Croatian Science Foundation under the project IP-2014-09-4882.

References

  1. 1.
    IEEE Standard for Floating-Point Arithmetic. IEEE Std 754-2008 pp. 1–70 (2008)Google Scholar
  2. 2.
    Aggarwal, C.C.: Outlier Analysis. Springer Publishing Company, Incorporated (2013)Google Scholar
  3. 3.
    Cao, V.L., Nicolau, M., McDermott, J.: One-Class Classification for Anomaly Detection with Kernel Density Estimation and Genetic Programming. In: Genetic Programming - 19th European Conference, EuroGP 2016, Porto, Portugal, March 30 - April 1, 2016, Proceedings, pp. 3–18 (2016)Google Scholar
  4. 4.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011). Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm CrossRefGoogle Scholar
  5. 5.
    Curry, R., Heywood, M.I.: One-Class Genetic Programming. In: Genetic Programming, 12th European Conference, EuroGP 2009, Tübingen, Germany, April 15–17, 2009, Proceedings, pp. 1–12 (2009)Google Scholar
  6. 6.
    Eddy, W.M.: Defenses Against TCP SYN Flooding Attacks - The Internet Protocol Journal - Volume 9, Number 4 (2017). URL http://www.cisco.com/c/en/us/about/press/internet-protocol-journal/back-issues/table-contents-34/syn-flooding-attacks.html
  7. 7.
    Elsayed, S., Sarker, R., Slay, J.: Evaluating the performance of a differential evolution algorithm in anomaly detection. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 2490–2497 (2015)Google Scholar
  8. 8.
    Folino, G., Pizzuti, C., Spezzano, G.: GP Ensemble for Distributed Intrusion Detection Systems. In: S. Singh, M. Singh, C. Apte, P. Perner (eds.) Pattern Recognition and Data Mining: Third International Conference on Advances in Pattern Recognition, ICAPR 2005, Bath, UK, August 22–25, 2005, Proceedings, Part I, pp. 54–62. Springer Berlin Heidelberg, Berlin, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Habibi, A., et al.: UNB ISCX NSL-KDD DataSet (2017). URL http://nsl.cs.unb.ca/NSL-KDD/
  10. 10.
    Jakobovic, D., et al.: Evolutionary Computation Framework (2016). URL http://ecf.zemris.fer.hr/
  11. 11.
    Khan, S.S., Madden, M.G.: One-Class Classification: Taxonomy of Study and Review of Techniques. CoRR abs/1312.0049 (2013). URL http://arxiv.org/abs/1312.0049
  12. 12.
    Kuzmanovic, A., Knightly, E.W.: Low-rate tcp-targeted denial of service attacks: the shrew vs. the mice and elephants. In: Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications, pp. 75–86. ACM (2003)Google Scholar
  13. 13.
    Ni, X., He, D., Chan, S., Ahmad, F.: Network Anomaly Detection Using Unsupervised Feature Selection and Density Peak Clustering. In: M. Manulis, A.R. Sadeghi, S. Schneider (eds.) Applied Cryptography and Network Security: 14th International Conference, ACNS 2016, Guildford, UK, June 19–22, 2016. Proceedings, pp. 212–227. Springer International Publishing, Cham (2016)Google Scholar
  14. 14.
    Orfila, A., Estevez-Tapiador, J.M., Ribagorda, A.: Evolving High-Speed, Easy-to-Understand Network Intrusion Detection Rules with Genetic Programming. In: M. Giacobini, A. Brabazon, S. Cagnoni, G.A. Di Caro, A. Ekárt, A.I. Esparcia-Alcázar, M. Farooq, A. Fink, P. Machado (eds.) Applications of Evolutionary Computing: EvoWorkshops 2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG, Tübingen, Germany, April 15–17, 2009. Proceedings, pp. 93–98. Springer Berlin Heidelberg, Berlin, Heidelberg (2009)Google Scholar
  15. 15.
    Overton, M.L.: Numerical Computing with IEEE Floating Point Arithmetic. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA (2001)Google Scholar
  16. 16.
    Poli, R., Langdon, W.B., McPhee, N.F.: A field guide to genetic programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk (2008). (With contributions by J. R. Koza)
  17. 17.
    R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008). URL http://www.R-project.org. ISBN 3-900051-07-0
  18. 18.
    Schölkopf, B., Platt, J.C., Shawe-Taylor, J.C., Smola, A.J., Williamson, R.C.: Estimating the Support of a High-Dimensional Distribution. Neural Comput. 13(7), 1443–1471 (2001)CrossRefGoogle Scholar
  19. 19.
    Shiravi, A., Shiravi, H., Tavallaee, M., Ghorbani, A.A.: Toward Developing a Systematic Approach to Generate Benchmark Datasets for Intrusion Detection. Comput. Secur. 31(3), 357–374 (2012)CrossRefGoogle Scholar
  20. 20.
    Song, D., Heywood, M.I., Zincir-Heywood, A.N.: Training genetic programming on half a million patterns: an example from anomaly detection. IEEE Trans. Evolutionary Computation 9(3), 225–239 (2005)CrossRefGoogle Scholar
  21. 21.
    Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A Detailed Analysis of the KDD CUP 99 Data Set. In: Proceedings of the Second IEEE International Conference on Computational Intelligence for Security and Defense Applications, CISDA’09, pp. 53–58. IEEE Press, Piscataway, NJ, USA (2009)Google Scholar
  22. 22.
    To, C., Elati, M.: A Parallel Genetic Programming for Single Class Classification. In: Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO ‘13 Companion, pp. 1579–1586. ACM, New York, NY, USA (2013)Google Scholar
  23. 23.
    Tsai, C.F., Hsu, Y.F., Lin, C.Y., Lin, W.Y.: Intrusion detection by machine learning: A review. Expert Systems with Applications 36(10), 11,994–12,000 (2009)CrossRefGoogle Scholar
  24. 24.
    Wang, W., Gombault, S., Guyet, T.: Towards Fast Detecting Intrusions: Using Key Attributes of Network Traffic. In: Proceedings of the 2008 The Third International Conference on Internet Monitoring and Protection, ICIMP ‘08, pp. 86–91. IEEE Computer Society, Washington, DC, USA (2008)Google Scholar
  25. 25.
    Wu, S.X., Banzhaf, W.: The Use of Computational Intelligence in Intrusion Detection Systems: A Review. Appl. Soft Comput. 10(1), 1–35 (2010)CrossRefGoogle Scholar
  26. 26.
    Zargari, S., Voorhis, D.: Feature Selection in the Corrected KDD-dataset. In: 2012 Third International Conference on Emerging Intelligent Data and Web Technologies, pp. 174–180 (2012)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Stjepan Picek
    • 1
    Email author
  • Erik Hemberg
    • 1
  • Domagoj Jakobovic
    • 2
  • Una-May O’Reilly
    • 1
  1. 1.MITCSAILCambridgeUSA
  2. 2.University of ZagrebFaculty of Electrical Engineering and ComputingZagrebCroatia

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