Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2015, Article ID 314601, 8 pages
http://dx.doi.org/10.1155/2015/314601
Research Article

Fusion of Heterogeneous Intrusion Detection Systems for Network Attack Detection

1Computer Science and Engineering, Kamaraj College of Engineering and Technology, Tamilnadu 626 001, India
2Information Technology, Mepco Schlenk Engineering College, Tamilnadu 626 005, India

Received 31 March 2015; Revised 15 June 2015; Accepted 1 July 2015

Academic Editor: Juan M. Corchado

Copyright © 2015 Jayakumar Kaliappan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. H.-J. Liao, C.-H. Richard Lin, Y.-C. Lin, and K.-Y. Tung, “Intrusion detection system: a comprehensive review,” Journal of Network and Computer Applications, vol. 36, no. 1, pp. 16–24, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. M. H. Bhuyan, D. K. Bhattacharyya, and J. K. Kalita, “Network anomaly detection: methods, systems and tools,” IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 303–336, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. H. G. Kayacik, A. N. Zincir-Heywood, and M. I. Heywood, “Selecting features for intrusion detection: A feature relevance analysis on KDD 99 intrusion detection datasets,” in Proceedings of the 3rd Annual Conference on Privacy, Security and Trust (PST '05), St. Andrews, Canada, October 2005. View at Scopus
  4. M. Woźniak, M. Graña, and E. Corchado, “A survey of multiple classifier systems as hybrid systems,” Information Fusion, vol. 16, no. 1, pp. 3–17, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. C. Thomas and N. Balakrishnan, “Improvement in intrusion detection with advances in sensor fusion,” IEEE Transactions on Information Forensics and Security, vol. 4, no. 3, pp. 542–551, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. G. Giacinto, F. Roli, and L. Didaci, “Fusion of multiple classifiers for intrusion detection in computer networks,” Pattern Recognition Letters, vol. 24, no. 12, pp. 1795–1803, 2003. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Siraj, R. B. Vaughn, and S. M. Bridges, “Intrusion sensor data fusion in an intelligent intrusion detection system architecture,” in Proceedings of the Hawaii International Conference on System Sciences, pp. 4437–4446, January 2004. View at Scopus
  8. D. Parikh and T. Chen, “Data fusion and cost minimization for intrusion detection,” IEEE Transactions on Information Forensics and Security, vol. 3, no. 3, pp. 381–389, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. G. Giacinto, R. Perdisci, M. Del Rio, and F. Roli, “Intrusion detection in computer networks by a modular ensemble of one-class classifiers,” Information Fusion, vol. 9, no. 1, pp. 69–82, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. Y. Li, J. Xia, S. Zhang, J. Yan, X. Ai, and K. Dai, “An efficient intrusion detection system based on support vector machines and gradually feature removal method,” Expert Systems with Applications, vol. 39, no. 1, pp. 424–430, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. A. Sung and S. Mukkamala, “Identifying important features for intrusion detection using support vector machines and neural networks,” in Proceedings of the Symposium on Applications and the Internet (SAINT '03), pp. 209–216, Orlando, Fla, USA. View at Publisher · View at Google Scholar
  12. Y. Li, J.-L. Wang, Z.-H. Tian, T.-B. Lu, and C. Young, “Building lightweight intrusion detection system using wrapper-based feature selection mechanisms,” Computers and Security, vol. 28, no. 6, pp. 466–475, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. S.-J. Horng, M.-Y. Su, Y.-H. Chen et al., “A novel intrusion detection system based on hierarchical clustering and support vector machines,” Expert Systems with Applications, vol. 38, no. 1, pp. 306–313, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. KDDCup dataset, 2014, http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.
  15. Weka, Waikato environment for knowledge analysis (weka) version 3.6, 2014, http://www.cs.waikato.ac.nz/ml/weka/.