Advertisement

A Novel Fuzzy Min-Max Neural Network and Genetic Algorithm-Based Intrusion Detection System

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 380)

Abstract

Today in the era of ICT, security of data and services on the WWW has become the most important issue for web service providers. Loopholes in the security systems of WWW may break the integrity, reliability, and availability of data and services. Today, intrusion detection systems based on data mining is the best security framework for the Internet. In this paper a novel intrusion detection system is proposed which is based on the fuzzy min-max neural network and the genetic algorithm. The proposed model is trained using fuzzy min-max neural network and the learning system is optimized by application of genetic algorithm. The developed system is tested on the KDD Cup dataset. The parameters classification accuracy and classification error were used as a final performance evaluator of the learning process. The experimental results show that the proposed model gives superior performance over other existing frameworks.

Keywords

IDS Anomaly detection Misuse detection FMM NN Genetic algorithm 

References

  1. 1.
    Liao, H.J., Lin, C.H.R., Lin, Y.C., Tung, K.Y.: Intrusion detection system: a comprehensive review. J. Netw. Comput. Appl. 36(1), 16–24 (2013)CrossRefGoogle Scholar
  2. 2.
    Yang, H., Li, T., Hu, X., Wang, F., Zou, Y.: A survey of artificial immune system based intrusion detection. Sci. World J. 11, 1–5 (2014)Google Scholar
  3. 3.
    Tong, X., Wang, Z., Haining, Y.: A research using hybrid RBF/Elman neural networks for intrusion detection system secure model. Comput. Phys. Commun. 180(10), 1795–1801 (2009)CrossRefGoogle Scholar
  4. 4.
    Wang, G., Hao, J., Ma, J., Huang, L.: A new approach to intrusion detection using artificial neural networks and fuzzy clustering. Expert Syst. Appl. 37(9), 6225–6232 (2010)CrossRefGoogle Scholar
  5. 5.
    Lei, J.Z., Ghorbani, A.A.: Improved competitive learning neural networks for network intrusion and fraud detection. Neurocomputing 75(1), 135–145 (2012)CrossRefGoogle Scholar
  6. 6.
    Shun, J., Malki, H.A.: Network intrusion detection system using neural networks. In: Fourth International Conference on Natural Computation, 2008, ICNC’08, vol. 5, pp. 242–246. IEEE (2008)Google Scholar
  7. 7.
    Sarasamma, S.T., Zhu, Q.A., Huff, J.: Hierarchical Kohonenen net for anomaly detection in network security. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 35(2), 302–312 (2005)CrossRefGoogle Scholar
  8. 8.
    Linda, O., Vollmer, T., Manic, M.: Neural network based intrusion detection system for critical infrastructures. In: International Joint Conference on Neural Networks, 2009, IJCNN 2009, pp. 1827–1834. IEEE (2009)Google Scholar
  9. 9.
    Joo, D., Hong, T., Han, I.: The neural network models for IDS based on the asymmetric costs of false negative errors and false positive errors. Expert Syst. Appl. 25(1), 69–75 (2003)CrossRefGoogle Scholar
  10. 10.
    Zhou, Z.H., Jiang, Y.: NeC4.5: neural ensemble based C4.5. IEEE Trans. Knowl. Data Eng. 16(6), 770–773 (2004)Google Scholar
  11. 11.
    Simpson, P.K.: Fuzzy min-max neural networks. I. Classification. IEEE Trans. Neural Netw. 3(5), 776–786 (1992)Google Scholar
  12. 12.
    Freitas, A.A.: Evolutionary algorithms for data mining. In: Data Mining and Knowledge Discovery Handbook, pp. 435–467. Springer, New York (2005)Google Scholar
  13. 13.
  14. 14.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  15. 15.
    Pfahringer, B.: Winning the KDD99 classification cup: bagged boosting. ACM SIGKDD Explor. Newslett. 1(2), 65–66 (2000)Google Scholar
  16. 16.
    Levin, I.: KDD-99 classifier learning contest: LLSoft’s results overview. SIGKDD Explor. 1(2), 67–75 (2000)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringBirla Institute of TechnologyMesraIndia

Personalised recommendations