a ddos attack detection method based on hybrid heterogeneous multiclassifier ensemble learning

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ID: 247911
2017
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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, k-Nearest Neighbor (k-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.
Reference Key
jia2017journala Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Bin Jia;Xiaohong Huang;Rujun Liu;Yan Ma
Journal Molecular diversity
Year 2017
DOI
10.1155/2017/4975343
URL
http://dx.doi.org/10.1155/2017/4975343
Keywords
computer engineering computer hardware

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