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Mobile Information Systems
Volume 2015, Article ID 260594, 13 pages
http://dx.doi.org/10.1155/2015/260594
Research Article

EVFDT: An Enhanced Very Fast Decision Tree Algorithm for Detecting Distributed Denial of Service Attack in Cloud-Assisted Wireless Body Area Network

1National University of Sciences and Technology, Islamabad 44000, Pakistan
2King Saud University, Riyadh 11451, Saudi Arabia

Received 12 May 2015; Accepted 9 August 2015

Academic Editor: Basit Shahzad

Copyright © 2015 Rabia Latif 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. R. Latif, H. Abbas, S. Assar, and S. Latif, “Analyzing feasibility for deploying very fast decision tree For DDoS attack detection in cloud-assisted WBAN,” in Intelligent Computing Theory: Proceedings of the 10th International Conference, ICIC 2014, Taiyuan, China, August 3-6, 2014, Lecture Notes in Computer Science, pp. 507–519, Springer, Berlin, Germany, 2014. View at Publisher · View at Google Scholar
  2. R. Latif, H. Abbas, and S. Assar, “Distributed denial of service (DDoS) attack in cloud- assisted wireless body area networks: a systematic literature review,” Journal of Medical Systems, vol. 38, article 128, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. P. Domingos and G. Hulten, “Mining high-speed data streams,” in Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71–80, Boston, Mass, USA, August 2000. View at Scopus
  4. S. T. Zargar, J. Joshi, and D. Tipper, “A survey of defense mechanisms against distributed denial of service (DDOS) flooding attacks,” IEEE Communications Surveys & Tutorials, vol. 15, no. 4, pp. 2046–2069, 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. D. Arora, P. Singh, and V. Singh, “Impact analysis of denial of service (DoS) due to packet flooding,” International Journal of Engineering Research and Applications, vol. 4, no. 6, pp. 144–149, 2014. View at Google Scholar
  6. T. Subbulakshmi, S. M. Shalinie, V. Ganapathisubramanian, K. Balakrishnan, D. Anandkumar, and K. Kannathal, “Detection of DDoS attacks using enhanced support vector machines with real time generated dataset,” in Proceedings of the 3rd International Conference on Advanced Computing (ICoAC '11), pp. 17–22, IEEE, Chennai, India, December 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. Y.-C. Wu, H.-R. Tseng, W. Yang, and R.-H. Jan, “Ddos detection and traceback with decision tree and grey relational analysis,” International Journal of Ad Hoc and Ubiquitous Computing, vol. 7, no. 2, pp. 121–136, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. S. M. Lee, D. S. Kim, J. H. Lee, and J. S. Park, “Detection of DDoS attacks using optimized traffic matrix,” Computers and Mathematics with Applications, vol. 63, no. 2, pp. 501–510, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. R. K. Arun and S. Selvakumar, “Detection of distributed denial of service attacks using an ensemble of adaptive and hybrid neuro-fuzzy systems,” Computer Communications, vol. 36, no. 3, pp. 303–319, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. T. Thwe and P. Thandar, “Statistical anomaly detection of DDoS attacks using K-nearest neighbour,” International Journal of Computer & Communication Engineering Research, vol. 2, no. 1, pp. 315–319, 2014. View at Google Scholar
  11. R. Latif, H. Abbas, and S. Assar, “Distributed Denial of Service (DDoS) attack in cloud—assisted wireless body area networks: a systematic literature review,” Journal of Medical Systems, vol. 38, article 12, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. G. Hulten, L. Spencer, and P. Domingos, “Mining time-changing data streams,” in Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '01), pp. 97–106, San Francisco, Calif, USA, August 2001. View at Scopus
  13. H. Yang and S. Fong, “Moderated VFDT in stream mining using adaptive tie threshold and incremental pruning,” in Proceedings of the 13th International Conference on Data Warehousing and Knowledge Discovery (DaWaK '11), pp. 471–483, Toulouse, France, August 2011.
  14. A. Fawzy, H. M. O. Mokhtar, and O. Hegazy, “Outliers detection and classification in wireless sensor networks,” Egyptian Informatics Journal, vol. 14, no. 2, pp. 157–164, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. VFML (Very Fast Machine Learning) toolkit, 2014, http://www.cs.washington.edu/dm/vfml/.
  16. L. Hughes, X. Wang, and T. Chen, “A review of protocol implementations and energy efficient cross-layer design for wireless body area networks,” Sensors, vol. 12, no. 11, pp. 14730–14773, 2012. View at Publisher · View at Google Scholar · View at Scopus