Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection
文献类型：期刊论文
| 作者 | Hu, Weiming1 ; Gao, Jun1; Wang, Yanguo2; Wu, Ou1; Maybank, Stephen3; Weiming Hu
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| 刊名 | IEEE TRANSACTIONS ON CYBERNETICS
 |
| 出版日期 | 2014 |
| 卷号 | 44期号:1页码:66-82 |
| 关键词 | Dynamic distributed detection network intrusions online Adaboost learning parameterized model |
| 英文摘要 | Current network intrusion detection systems lack adaptability to the frequently changing network environments. Furthermore, intrusion detection in the new distributed architectures is now a major requirement. In this paper, we propose two online Adaboost-based intrusion detection algorithms. In the first algorithm, a traditional online Adaboost process is used where decision stumps are used as weak classifiers. In the second algorithm, an improved online Adaboost process is proposed, and online Gaussian mixture models (GMMs) are used as weak classifiers. We further propose a distributed intrusion detection framework, in which a local parameterized detection model is constructed in each node using the online Adaboost algorithm. A global detection model is constructed in each node by combining the local parametric models using a small number of samples in the node. This combination is achieved using an algorithm based on particle swarm optimization (PSO) and support vector machines. The global model in each node is used to detect intrusions. Experimental results show that the improved online Adaboost process with GMMs obtains a higher detection rate and a lower false alarm rate than the traditional online Adaboost process that uses decision stumps. Both the algorithms outperform existing intrusion detection algorithms. It is also shown that our PSO, and SVM-based algorithm effectively combines the local detection models into the global model in each node; the global model in a node can handle the intrusion types that are found in other nodes, without sharing the samples of these intrusion types. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | ANOMALY DETECTION ; NEURAL-NETWORKS ; DETECTION SYSTEMS ; DETECTION MODEL ; CLASSIFIERS ; ALGORITHM |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000328948900005 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3264]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_视频内容安全团队 |
| 通讯作者 | Weiming Hu |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.China Acad Railway Sci, Inst Infrastruct Inspect, Beijing 100190, Peoples R China 3.Univ London Birkbeck Coll, Sch Comp Sci & Informat Syst, London WC1E 7HX, England |
| 推荐引用方式 GB/T 7714 | Hu, Weiming,Gao, Jun,Wang, Yanguo,et al. Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection[J]. IEEE TRANSACTIONS ON CYBERNETICS,2014,44(1):66-82. |
| APA | Hu, Weiming,Gao, Jun,Wang, Yanguo,Wu, Ou,Maybank, Stephen,&Weiming Hu.(2014).Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection.IEEE TRANSACTIONS ON CYBERNETICS,44(1),66-82. |
| MLA | Hu, Weiming,et al."Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection".IEEE TRANSACTIONS ON CYBERNETICS 44.1(2014):66-82. |
入库方式： OAI收割
来源：自动化研究所
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