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Journal of Electrical and Computer Engineering
Volume 2017, Article ID 4975343, 9 pages
https://doi.org/10.1155/2017/4975343
Research Article

A DDoS Attack Detection Method Based on Hybrid Heterogeneous Multiclassifier Ensemble Learning

1Information and Network Center, Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
2School of CyberSpace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China

Correspondence should be addressed to Bin Jia; nc.ude.tpub@0102dq_bj

Received 22 October 2016; Accepted 11 January 2017; Published 15 March 2017

Academic Editor: Jun Bi

Copyright © 2017 Bin Jia 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.

Abstract

The explosive growth of network traffic and its multitype on Internet have brought new and severe challenges to DDoS attack detection. To get the higher True Negative Rate (TNR), accuracy, and precision and to guarantee the robustness, stability, and universality of detection system, in this paper, we propose a DDoS attack detection method based on hybrid heterogeneous multiclassifier ensemble learning and design a heuristic detection algorithm based on Singular Value Decomposition (SVD) to construct our detection system. Experimental results show that our detection method is excellent in TNR, accuracy, and precision. Therefore, our algorithm has good detective performance for DDoS attack. Through the comparisons with Random Forest, -Nearest Neighbor (-NN), and Bagging comprising the component classifiers when the three algorithms are used alone by SVD and by un-SVD, it is shown that our model is superior to the state-of-the-art attack detection techniques in system generalization ability, detection stability, and overall detection performance.