Table of Contents Author Guidelines Submit a Manuscript
Security and Communication Networks
Volume 2017 (2017), Article ID 6216078, 15 pages
https://doi.org/10.1155/2017/6216078
Research Article

A Fusion of Multiagent Functionalities for Effective Intrusion Detection System

1Department of ECE, Kalasalingam University, Krishnankoil, Tamil Nadu 626126, India
2Department of Instrumentation & Control Engineering, Kalasalingam University, Krishnankoil, Tamil Nadu, India

Correspondence should be addressed to Dhanalakshmi Krishnan Sadhasivan; moc.liamg@3iaj.imhskalanahd

Received 30 June 2016; Revised 17 September 2016; Accepted 10 October 2016; Published 11 January 2017

Academic Editor: Zheng Yan

Copyright © 2017 Dhanalakshmi Krishnan Sadhasivan and Kannapiran Balasubramanian. 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. Y. Zhang, L. Wang, W. Sun, R. C. Green II, and M. Alam, “Distributed intrusion detection system in a multi-layer network architecture of smart grids,” IEEE Transactions on Smart Grid, vol. 2, no. 4, pp. 796–808, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Shamshirband, N. B. Anuar, M. L. M. Kiah, and A. Patel, “An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique,” Engineering Applications of Artificial Intelligence, vol. 26, no. 9, pp. 2105–2127, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. C.-L. Lui, T.-C. Fu, and T.-Y. Cheung, “Agent-based network intrusion detection system using data mining approaches,” in Proceedings of the 3rd International Conference on Information Technology and Applications (ICITA '05), pp. 131–136, Sydney, Australia, July 2005. View at Scopus
  4. A. Chauhan, G. Mishra, and G. Kumar, “Survey on data mining techniques in intrusion detection,” International Journal of Scientific & Engineering Research, vol. 2, no. 7, pp. 1–4, 2011. View at Google Scholar
  5. J. J. Davis and A. J. Clark, “Data preprocessing for anomaly based network intrusion detection: a review,” Computers & Security, vol. 30, no. 6-7, pp. 353–375, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. S. A. Joshi and V. S. Pimprale, “Network Intrusion Detection System (NIDS) based on data mining,” International Journal of Engineering Science and Innovative Technology, vol. 2, no. 1, pp. 95–98, 2013. View at Google Scholar
  7. E. W. T. Ferreira, G. A. Carrijo, R. de Oliveira, and N. V. de Souza Araujo, “Intrusion detection system with wavelet and neural artifical network approach for networks computers,” IEEE Latin America Transactions, vol. 9, no. 5, pp. 832–837, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. G. V. Nadiammai and M. Hemalatha, “Effective approach toward Intrusion Detection System using data mining techniques,” Egyptian Informatics Journal, vol. 15, no. 1, pp. 37–50, 2014. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Singh, G. Mehta, C. Vaid, and P. Oberoi, “Detection of malicious node in wireless sensor network based on data mining,” in Proceedings of the International Conference on Computing Sciences (ICCS '12), pp. 291–294, September 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. M. A. Faisal, Z. Aung, J. R. Williams, and A. Sanchez, “Data-stream-based intrusion detection system for advanced metering infrastructure in smart grid: a feasibility study,” IEEE Systems Journal, vol. 9, no. 1, pp. 31–44, 2015. View at Publisher · View at Google Scholar · View at Scopus
  11. P. Shrivastava and H. Gupta, “A review of density-based clustering in spatial data,” International Journal of Advanced Computer Research, vol. 2, pp. 200–202, 2012. View at Google Scholar
  12. S. Ganapathy, K. Kulothungan, P. Yogesh, and A. Kannan, “A novel weighted fuzzy C-means clustering based on immune genetic algorithm for intrusion detection,” Procedia Engineering, vol. 38, pp. 1750–1757, 2012. View at Publisher · View at Google Scholar
  13. M. Panda, A. Abraham, and M. R. Patra, “A hybrid intelligent approach for network intrusion detection,” Procedia Engineering, vol. 30, pp. 1–9, 2012. View at Google Scholar
  14. M. Govindarajan and V. Abinaya, “An outlier detection approach with data mining in wireless sensor network,” International Journal of Current Engineering and Technology, vol. 4, pp. 929–932, 2014. View at Google Scholar
  15. S. S. Sivatha Sindhu, S. Geetha, and A. Kannan, “Decision tree based light weight intrusion detection using a wrapper approach,” Expert Systems with Applications, vol. 39, no. 1, pp. 129–141, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. I. Butun, S. D. Morgera, and R. Sankar, “A survey of intrusion detection systems in wireless sensor networks,” IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 266–282, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Xu, J. Wang, S. Xie, W. Chen, and J.-U. Kim, “Study on intrusion detection policy for wireless sensor networks,” International Journal of Security and its Applications, vol. 7, no. 1, pp. 1–6, 2013. View at Google Scholar · View at Scopus
  18. P. Louvieris, N. Clewley, and X. Liu, “Effects-based feature identification for network intrusion detection,” Neurocomputing, vol. 121, pp. 265–273, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. N. A. Alrajeh, S. Khan, and B. Shams, “Intrusion detection systems in wireless sensor networks: a review,” International Journal of Distributed Sensor Networks, vol. 9, no. 5, Article ID 167575, 2013. View at Publisher · View at Google Scholar
  20. L. Coppolino, S. D'Antonio, A. Garofalo, and L. Romano, “Applying data mining techniques to Intrusion Detection in Wireless Sensor Networks,” in Proceedings of the 8th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC '13), pp. 247–254, October 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. A. Biswas, M. Sharma, T. Poddder, and N. Kar, “An approach towards multilevel and multiagent based intrusion detection system,” in Proceedings of the IEEE International Conference on Advanced Communication, Control and Computing Technologies (ICACCCT '14), pp. 1787–1790, IEEE, Ramanathapuram, India, May 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. W. Wang, T. Guyet, R. Quiniou, M.-O. Cordier, F. Masseglia, and X. Zhang, “Autonomic intrusion detection: adaptively detecting anomalies over unlabeled audit data streams in computer networks,” Knowledge-Based Systems, vol. 70, pp. 103–117, 2014. View at Publisher · View at Google Scholar · View at Scopus
  23. T. Morris, A. Srivastava, B. Reaves, W. Gao, K. Pavurapu, and R. Reddi, “A control system testbed to validate critical infrastructure protection concepts,” International Journal of Critical Infrastructure Protection, vol. 4, no. 2, pp. 88–103, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. S. Pan, T. Morris, and U. Adhikari, “A specification-based intrusion detection framework for cyber-physical environment in electric power system,” International Journal of Network Security, vol. 17, no. 2, pp. 174–188, 2015. View at Google Scholar · View at Scopus
  25. S. Pan, T. Morris, and U. Adhikari, “Developing a hybrid intrusion detection system using data mining for power systems,” IEEE Transactions on Smart Grid, vol. 6, no. 6, pp. 3104–3113, 2015. View at Publisher · View at Google Scholar
  26. S. Pan, T. Morris, and U. Adhikari, “Classification of disturbances and cyber-attacks in power systems using heterogeneous time-synchronized data,” IEEE Transactions on Industrial Informatics, vol. 11, no. 3, pp. 650–662, 2015. View at Publisher · View at Google Scholar · View at Scopus
  27. M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the KDD CUP 99 data set,” in Proceedings of the 2nd IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA '09), July 2009. View at Publisher · View at Google Scholar · View at Scopus