NDLI: Exploiting efficient data mining techniques to enhance intrusion detection systems

Content Provider	IEEE Xplore Digital Library
Author	Lu, C.-T. Boedihardjo, A.P. Manalwar, P.
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Sci., Virginia Polytech. Inst. & State Univ., Blacksburg, VA, USA (Lu, C.-T.; Boedihardjo, A.P.; Manalwar, P.)
Abstract	Security is becoming a critical part of organizational information systems. Intrusion detection system (IDS) is an important detection that is used as a countermeasure to preserve data integrity and system availability from attacks. Data mining is being used to clean, classify, and examine large amount of network data to correlate common infringement for intrusion detection. The main reason for using data mining techniques for intrusion detection systems is due to the enormous volume of existing and newly appearing network data that require processing. The amount of data accumulated each day by a network is huge. Several data mining techniques such as clustering, classification, and association rules are proving to be useful for gathering different knowledge for intrusion detection. This paper presents the idea of applying data mining techniques to intrusion detection systems to maximize the effectiveness in identifying attacks, thereby helping the users to construct more secure information systems.
Sponsorship	IEEE Syst., Man and Cybernetics Soc. (IEEE SMC)
Starting Page	512
Ending Page	517
File Size	134386
Page Count	6
File Format	PDF
ISBN	0780390938
DOI	10.1109/IRI-05.2005.1506525
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Intrusion detection Information systems Computer security Data security Information security Protection Availability Monitoring Information analysis
Content Type	Text
Resource Type	Article

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