Evaluating Sequential Combination of Two Genetic Algorithm-Based Solutions for Intrusion Detection

  • Zorana Banković
  • Slobodan Bojanić
  • Octavio Nieto-Taladriz
Part of the Advances in Soft Computing book series (AINSC, volume 53)


The paper presents a serial combination of two genetic algorithm-based intrusion detection systems. Feature extraction techniques are deployed in order to reduce the amount of data that the system needs to process. The designed system is simple enough not to introduce significant computational overhead, but at the same time is accurate, adaptive and fast. There is a large number of existing solutions based on machine learning techniques, but most of them introduce high computational overhead. Moreover, due to its inherent parallelism, our solution offers a possibility of implementation using reconfigurable hardware with the implementation cost much lower than the one of the traditional systems. The model is verified on KDD99 benchmark dataset, generating a solution competitive with the solutions of the state-of-the-art.


intrusion detection genetic algorithm sequential combination principal component analysis multi expression programming 


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  1. 1.
    Banković, Z., Stepanović, D., Bojanić, S., Nieto-Taladriz, O.: Improving Network Security Using Genetic Algorithm Approach. Computers & Electrical Engineering 33(5-6), 438–451Google Scholar
  2. 2.
    Grosan, C., Abraham, A., Chis, M.: Computational Intelligence for light weight intrusion detection systems. In: International Conference on Applied Computing (IADIS 2006), San Sebastian, Spain, pp. 538–542 (2006); ISBN: 9728924097Google Scholar
  3. 3.
    Gong, R.H., Zulkernine, M., Abolmaesumi, P.: A Software Implementation of a Genetic Algorithm Based Approach to Network Intrusion Detection. In: Proceedings of SNPD/SAWN 2005 (2005)Google Scholar
  4. 4.
    Chittur, A.: Model Generation for an Intrusion Detection System Using Genetic Algorithms (accessed in 2006),
  5. 5.
    Weiss, G.: Mining with rarity: A unifying framework. SIGKDD Explorations 6(1), 7–19 (2004)CrossRefGoogle Scholar
  6. 6.
  7. 7.
    McHugh, J.: Testing Intrusion Detection Systems: A Critique of the 1998 and 1999 DARPA IDS Evaluation as Performed by Lincoln Laboratory. ACM Trans. on Information and System security 3(4), 262–294 (2000)CrossRefGoogle Scholar
  8. 8.
    Bouzida, Y., Cuppens, F.: Detecting known and novel network intrusion. In: IFIP/SEC 2006 21st International Information Security Conference, Karlstad, Sweden (2006)Google Scholar
  9. 9.
    Goldberg, D.E.: Genetic algorithms for search, optimization, and machine learning. Addison-Wesley, Reading (1989)Google Scholar
  10. 10.
    GAlib, A.: C++ Library of Genetic Algorithm Components,
  11. 11.
    Pan, Z., Chen, S., Hu, G., Zhang, D.: Hybrid Neural Network and C4.5 for Misuse Detection. In: Proceedings of the Second International Conference on Machine Learning and Cybernetics, November 2003, vol. 4, pp. 2463–2467 (2003)Google Scholar
  12. 12.
    Folino, G., Pizzuti, C., Spezzano, G.: GP Ensemble for Distributed Intrusion Detection Systems. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR 2005. LNCS, vol. 3686. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Laskov, P., Düssel, P., Schäfer, C., Rieck, K.: Learning Intrusion Detection: Supervised or Unsaupervised? In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 50–57. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    Yao, J.T., Zhao, S.L., Saxton, L.V.: A Study on Fuzzy Intrusion Detection. Data mining, intrusion detection, information assurance and data networks security (2005)Google Scholar
  15. 15.
    Chawla, N.V., Lazarevic, A., Hall, L.O., Bowyer, K.: SMOTEBoost: Improving prediction of the minority class in boosting. In: Proceedings of Principles of Knowledge Discovery in Databases (2003)Google Scholar
  16. 16.
    Fodor, I.K.: A Survey of Dimension Reduction Techniques,

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Zorana Banković
    • 1
  • Slobodan Bojanić
    • 1
  • Octavio Nieto-Taladriz
    • 1
  1. 1.ETSI TelecomunicaciónUniversidad Politécnica de MadridMadridSpain

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