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Genetic Programming Feature Extraction with Different Robust Classifiers for Network Intrusion Detection

by Khaled Badran, Alaa Rohim
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 168 - Number 1
Year of Publication: 2017
Authors: Khaled Badran, Alaa Rohim
10.5120/ijca2017914276

Khaled Badran, Alaa Rohim . Genetic Programming Feature Extraction with Different Robust Classifiers for Network Intrusion Detection. International Journal of Computer Applications. 168, 1 ( Jun 2017), 37-43. DOI=10.5120/ijca2017914276

@article{ 10.5120/ijca2017914276,
author = { Khaled Badran, Alaa Rohim },
title = { Genetic Programming Feature Extraction with Different Robust Classifiers for Network Intrusion Detection },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2017 },
volume = { 168 },
number = { 1 },
month = { Jun },
year = { 2017 },
issn = { 0975-8887 },
pages = { 37-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume168/number1/27841-2017914276/ },
doi = { 10.5120/ijca2017914276 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:14:59.722290+05:30
%A Khaled Badran
%A Alaa Rohim
%T Genetic Programming Feature Extraction with Different Robust Classifiers for Network Intrusion Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 168
%N 1
%P 37-43
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we compare the performance of three traditional robust classifiers (Neural Networks, Support Vector Machines, and Decision Trees) with and without utilizing multi-objective genetic programming in the feature extraction phase. This work argues that effective feature extraction can significantly enhance the performance of these classifiers. We have applied these three classifiers stand alone to real world five datasets from the UCI machine learning database and also to network intrusion “KDD-99 cup” dataset. Then, the experiments were repeated by adding the feature extraction phase. The results of the two approaches are compared and conclude that the effective method is to evolve optimal feature extractors that transform input pattern space into a decision space in which the performance of traditional robust classifiers can be enhanced.

References
  1. Y Zhang and P I Rockett “Domain-Independent Approaches to Optimise Feature Extraction for MultiClassification using Multi-Objective Genetic Programming” Technical Report No. VIE 2007/001 Department of Electronic and Electrical Engineering University of Sheffield
  2. Y. Liu, F. Tang, and Z. Zeng, “Feature selection based on dependency margin,” IEEE Trans. Cybern., vol. 45, no. 6, pp. 1209–1221, Jun. 2015.
  3. H. Liu and Z. Zhao, “Manipulating data and dimension reduction methods: Feature selection,” in Encyclopedia of Complexity and Systems Science. Berlin, Germany: Springer, 2009, pp. 5348–5359.
  4. H. Liu, H. Motoda, R. Setiono, and Z. Zhao, “Feature selection: An ever evolving frontier in data mining,” in Proc. JMLR Feature Sel. Data Min., vol. 10. Hyderabad, India, 2010, pp. 4–13.
  5. H. Liu and L. Yu, “Toward integrating feature selection algorithms for classification and clustering,” IEEE Trans. Knowl. Data Eng., vol. 17, no. 4, pp. 491–502, Apr. 2005.
  6. J. Koza “ Genetic programming: On the programming of computers by means of natural selection MIT Press, Cambridge, Massachusetts (1992)
  7. C. Darwin “ On the origin of species by means of natural selection or the preservation of favoured races in the struggle for life” Cambridge University Press, Cambridge, UK (1864)
  8. Peter A. Whigham, and Grant Dick, “Implicitly Controlling Bloat in Genetic Programming,” IEEE Transaction on Evolutionary Computation, Vol. 14, No. 2, APRIL 2010, pp. 173-190.
  9. M. J. Streeter, “The root causes of code growth in genetic programming,” in Proc. Genet. Programming (EuroGP ’03), vol. 2610. Essex: SpringerVerlag, Apr. 14–16, 2003, pp. 443–454
  10. H. Stringer and A. Wu, “Bloat is unnatural: An analysis of changes invariable chromosome length absent selection pressure,” Univ. Central Florida, Tech. Rep. CS-TR-04-01, 2004.
  11. C. Skinner, P. J. Riddle, and C. Triggs, “Mathematics prevents bloat,” in Proc. 2005 IEEE Congr. Evol.Comput., vol. 1. Edinburgh, U.K.: IEEE Press, Sep.2–5, 2005, pp. 390–395
  12. Elsayed S, Sarker R, Essam D (2015) Survey of uses of evolutionary computation algorithms and swarm intelligence for network intrusion detection. International Journal of Computational Intelligence and Applications 14(04):1550,025, D
  13. H. Liu and Z. Zhao, “Manipulating data and dimension reduction methods: Feature selection,” in Encyclopedia of Complexity and Systems Science. Berlin, Germany: Springer, 2009, pp. 5348–5359.
  14. H. Liu, H. Motoda, R. Setiono, and Z. Zhao, “Feature selection: An ever evolving frontier in data mining,” in Proc. JMLR Feature Sel. DataMin., vol. 10. Hyderabad, India, 2010, pp. 4–13.
  15. B. Xue, M. Zhang, and W. N. Browne, “Particle swarm optimization for feature selection in classification: A multi-objective approach,” IEEETrans. Cybern., vol. 43, no. 6, pp. 1656–1671, Dec. 2013.
  16. Raymer, M., Punch, W., Goodman, E., & Kuhn, L.. Genetic programming for improved data mining: Application to the biochemistry of protein interactions. In Proceedings of the first annual conference on genetic programming (pp. 375–380). Cambridge, Massachusetts: MIT Press. (1996)
  17. Bot, M., & Langdon, W. Application of genetic programming to induction of linear classification trees. In Genetic programming, proceedings of EuroGP’2000 (pp. 247–258). Berlin, Heidelberg: Springer-Verlag. Chui, C. (1992). An introduction to wavelets. Boston: Academic Press.
  18. Kotani, M., Nakai, M., & Akazawa, K. . Feature extraction using evolutionary computation. In Proceedings of the 1999 congress on evolutionary computation, 1999. CEC 99 (Vol. 2, pp. 1230–1236).
  19. Raymer, M., Punch, W., Goodman, E., & Kuhn, L. Genetic programming for improved data mining: Application to the biochemistry of protein interactions. In Proceedings of the first annual conference on genetic programming (pp. 375–380). Cambridge, Massachusetts: MIT Press.1996
  20. Tackett, W. Genetic programming for feature discovery and image discrimination. In Proceedings of the fifth international conference on genetic algorithms, ICGA-93 (pp. 303–309). 1993
  21. Sherrah, J. Automatic feature extraction for pattern recognition. Ph.D. Thesis, The University of Adelaide.1998
  22. Krawiec, K. Genetic programming-based construction of features for machine learning and knowledge discovery tasks. Genetic Programming and Evolvable Machines, 3(4), 329–343.2002
  23. Cristina Vatamanu, Dragos Gavrilut, Razvan Benchea, Henri Luchian, "Feature Extraction Using Genetic Programming with Applications in Malware Detection", , vol. 00, no. , pp. 224-231, 2015.
  24. D. Song, M. Heywood and A. N. Zincir-Heywood, A linear genetic programming approach to intrusion detection, Genetic and Evolutionary Computation — GECCO 2003 (2003) 2325–2336.
  25. A. Abraham, C. Grosan and C. Martin-Vide, Evolutionary design of intrusion detection programs, Int. J. Netw. Security 4 (2007) 328–339.
  26. W. Lu and I. Traore, Detecting new forms of network intrusion using genetic programming, Comput. Intell. 20 (2004) 475–494.
  27. A. Boukelif and K. M. Faraoun, Genetic programming approach for multi-category pattern classification applied to network intrusions detection, Int. J. Comput. Intell. Appl. 6 (2006) 77–99
  28. K. Badran, P. Rockett, "Multi-class pattern classification using single multi-dimensional feature-space feature extraction evolved by multi-objective genetic programming and its application to network intrusion detection", Genetic Programming and Evolvable Machines, vol. 13, no. 1, pp. 33-63, 2012.
  29. Thi Anh Le, Thi Huong Chu, Quang Uy Nguyen, Xuan Hoai Nguyen, "Malware detection using genetic programming", Computational Intelligence for Security and Defense Applications (CISDA) 2014 Seventh IEEE Symposium on, pp. 1-6, 2014.
  30. Jorge Blasco, Agustin Orfila, Arturo Ribagorda “Improving Network Intrusion Detection by Means of Domain-Aware Genetic Programming” DOI 10.1109/ARES.2010.53 in IEEE 2010.
  31. Buitinck et al., “API design for machine learning software: experiences from the scikit-learn project”, 2013.
  32. KDD data set, 1999; http://kdd.ics.uci.edu/databases/- kddcup99/kddcup99.html
Index Terms

Computer Science
Information Sciences

Keywords

Genetic Programming Feature Extraction Neural Network Support Vector Machines Decision Tress.

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