A DDoS Attack Detection Method Based on Hybrid Heterogeneous Multiclassifier Ensemble Learning
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,...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2017/4975343 |
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| _version_ | 1832565889767047168 |
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| author | Bin Jia Xiaohong Huang Rujun Liu Yan Ma |
| author_facet | Bin Jia Xiaohong Huang Rujun Liu Yan Ma |
| author_sort | Bin Jia |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d7aab4082eea48c7abc79a2b0b503aee |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-d7aab4082eea48c7abc79a2b0b503aee2025-02-03T01:06:20ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552017-01-01201710.1155/2017/49753434975343A DDoS Attack Detection Method Based on Hybrid Heterogeneous Multiclassifier Ensemble LearningBin Jia0Xiaohong Huang1Rujun Liu2Yan Ma3Information and Network Center, Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaInformation and Network Center, Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of CyberSpace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaInformation and Network Center, Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaThe 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.http://dx.doi.org/10.1155/2017/4975343 |
| spellingShingle | Bin Jia Xiaohong Huang Rujun Liu Yan Ma A DDoS Attack Detection Method Based on Hybrid Heterogeneous Multiclassifier Ensemble Learning Journal of Electrical and Computer Engineering |
| title | A DDoS Attack Detection Method Based on Hybrid Heterogeneous Multiclassifier Ensemble Learning |
| title_full | A DDoS Attack Detection Method Based on Hybrid Heterogeneous Multiclassifier Ensemble Learning |
| title_fullStr | A DDoS Attack Detection Method Based on Hybrid Heterogeneous Multiclassifier Ensemble Learning |
| title_full_unstemmed | A DDoS Attack Detection Method Based on Hybrid Heterogeneous Multiclassifier Ensemble Learning |
| title_short | A DDoS Attack Detection Method Based on Hybrid Heterogeneous Multiclassifier Ensemble Learning |
| title_sort | ddos attack detection method based on hybrid heterogeneous multiclassifier ensemble learning |
| url | http://dx.doi.org/10.1155/2017/4975343 |
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