NDLI: Errors Estimating of Incompletion and Updating Strategy in IDS

Content Provider	IEEE Xplore Digital Library
Author	Fu-Xiong Sun
Copyright Year	2006
Description	Author affiliation: Inst. of Inf., Zhongnan Univ. of Econ. & Law, Wuhan (Fu-Xiong Sun)
Abstract	Because there existed the problem of incomplete training sets in current intrusion detection systems, it results in false positive errors. In the paper, an ID model-IAIDM (immune-based adaptive intrusion detection model) is firstly put forward. Based on the characteristics of IAIDM, a analytical method of discrete random process is introduced to estimate the ratio of false positive errors. The analytical results show that incomplete training sets mainly affect the peripheral regions of self space instead of the whole of sample space. According to the analytical results, an incremental algorithm (IA) is proposed to update incomplete training sets dynamically. The experiment results demonstrate IA algorithm can update local self space having changed instead of the whole space incrementally and dynamically so that IAIDM can adjust itself to the current network environment quickly
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	2948
Ending Page	2953
File Size	232218
Page Count	6
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.259144
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Intrusion detection Immune system Random processes Algorithm design and analysis Computer networks Proteins Fault detection Sun Environmental economics Error analysis natural immune system Intrusion detection system incompletion
Content Type	Text
Resource Type	Article

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