NDLI: An Optimized Intrusion Detection System Using PCA and BNN

Content Provider	IEEE Xplore Digital Library
Author	Dong Seong Kim Ha-Nam Nguyen Thandar Thein Jong Sou Park
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Eng., Hankuk Aviation Univ., Seoul (Dong Seong Kim)
Abstract	This paper proposes an optimized intrusion detection system (IDS) using principle component analysis (PCA) and back-propagation neural network (BNN). Existing neural network based IDS are mainly suffering from two problems: one is to determine the numbers of hidden layers and regulating weight values to configure its topology. The other is to process the large amounts of audit data. In order to increase detection rates and decrease the processing overheads, we exploit genetic algorithm (GA). The operation of GA enables IDS based on combination of PCA and BNN to increase their detection rates and decrease processing overheads. The experimental results on KDD 1999 intrusion detection dataset demonstrate the possibility of our approach
Sponsorship	Commun. Soc., IEICE
Starting Page	356
Ending Page	359
File Size	99206
Page Count	4
File Format	PDF
ISBN	4885522161
DOI	10.1109/APSITT.2005.203684
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-11-09
Publisher Place	Myanmar
Access Restriction	Subscribed
Rights Holder	Institute of Electronics, Information and Communication Engineers (IEICE)
Subject Keyword	Network topology Neural networks Laboratories Intrusion detection Information security Information processing Computer networks Computer security Principal component analysis Genetic algorithms
Content Type	Text
Resource Type	Article

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