Intrusion detection by machine learning: A review

NCUIR > School of Management at National Central University > Graduate Institute of Information Management > journal & Dissertation > Item 987654321/29663

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29663

Title:	Intrusion detection by machine learning: A review
Authors:	Tsai,CF;Hsu,YF;Lin,CY;Lin,WY
Contributors:	資訊管理研究所
Keywords:	NEAREST NEIGHBOR CLASSIFIER;DETECTION SYSTEM;COMPUTER-NETWORKS;NEURAL-NETWORKS;ALGORITHM;MODEL;IDS
Date:	2009
Issue Date:	2010-06-29 20:37:40 (UTC+8)
Publisher:	中央大學
Abstract:	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.
Relation:	EXPERT SYSTEMS WITH APPLICATIONS
Appears in Collections:	[Graduate Institute of Information Management] journal & Dissertation

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