Intrusion detection by machine learning: A review

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题名:	Intrusion detection by machine learning: A review
作者:	Tsai,CF;Hsu,YF;Lin,CY;Lin,WY
贡献者:	資訊管理研究所
关键词:	NEAREST NEIGHBOR CLASSIFIER;DETECTION SYSTEM;COMPUTER-NETWORKS;NEURAL-NETWORKS;ALGORITHM;MODEL;IDS
日期:	2009
上传时间:	2010-06-29 20:37:40 (UTC+8)
出版者:	中央大學
摘要:	The popularity of using Internet contains some risks of network attacks. Intrusion detection is one major research problem in network security, whose aim is to identify unusual access or attacks to secure internal networks. In literature, intrusion detection systems have been approached by various machine learning techniques. However, there is no a review paper to examine and understand the current status of using machine learning techniques to solve the intrusion detection problems. This chapter reviews 55 related studies in the period between 2000 and 2007 focusing on developing single, hybrid, and ensemble classifiers. Related studies are compared by their classifier design, datasets used, and other experimental setups. Current achievements and limitations in developing intrusion detection systems by machine learning are present and discussed. A number of future research directions are also provided. (C) 2009 Elsevier Ltd. All rights reserved.
關聯:	EXPERT SYSTEMS WITH APPLICATIONS
显示于类别:	[資訊管理研究所] 期刊論文

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