Learning vector quantization neural network method for network intrusion detection

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Date: Jan. 2007
Publisher: Springer
Document Type: Article
Length: 229 words

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Abstract :

Byline: Yang Degang (1,2), Chen Guo (3), Wang Hui (4), Liao Xiaofeng (1) Keywords: intrusion detection; learning vector quantization; neural network; feature extraction; TP 393 Abstract: A new intrusion detection method based on learning vector quantization (LVQ) with low overhead and high efficiency is presented. The computer vision system employs LVQ neural networks as classifier to recognize intrusion. The recognition process includes three stages: 1 feature selection and data normalization processing 2 learning the training data selected from the feature data set 3 identifying the intrusion and generating the result report of machine condition classification. Experimental results show that the proposed method is promising in terms of detection accuracy, computational expense and implementation for intrusion detection. Author Affiliation: (1) Department of Computer Science and Engineering, Chongqing University, Chongqing, 400044, China (2) Department of Mathematics and Computer Science, Chongqing Normal University, Chongqing, 400047, China (3) Department of Modern Educational Technology, Chongqing Normal University, Chongqing, 400047, China (4) Department of Mathematics, Leshan Normal College, Leshan, 610043, Sichuan, China Article History: Registration Date: 14/02/2006 Received Date: 20/04/2006 Article note: Foundation item: Supported by the National Natural Science Foundation of China (60573047), Natural Science Foundation of the Science and Technology Committee of Chongqing (8503) and the Applying Basic Research of the Education Committee of Chongqing (KJ060804) Biography: YANG Degang (1976--), male, Ph.D. candidate, Associate professor of Chongqing Normal University, research direction: information security.

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Gale Document Number: GALE|A206228412