Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 视频内容安全

	Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection
	Hu, Weiming1 ; Gao, Jun 1; Wang, Yanguo 2; Wu, Ou1 ; Maybank, Stephen 3; Weiming Hu
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
	2014
卷号	44 期号:1 页码:66-82
文章类型	Article
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.
关键词	Dynamic Distributed Detection Network Intrusions Online Adaboost Learning Parameterized Model
WOS标题词	Science & Technology ; Technology
关键词[WOS]	ANOMALY DETECTION ; NEURAL-NETWORKS ; DETECTION SYSTEMS ; DETECTION MODEL ; CLASSIFIERS ; ALGORITHM
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000328948900005
引用统计	被引频次：102[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3264
专题	多模态人工智能系统全国重点实验室_视频内容安全
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
第一作者单位	模式识别国家重点实验室
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.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
wmhutc.pdf（2100KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	浏览下载
wmhutomm.pdf（2030KB）			开放获取	--	浏览下载