DocumentCode :
1636530
Title :
The improvement of Transductive Support Vector Machine and its application to network intrusion detection
Author :
Man-fu, Yan ; Zhi-fang, Liu
Author_Institution :
Dept. of Math., Tangshan Teacher´´s Coll., Tangshan, China
fYear :
2010
Firstpage :
19
Lastpage :
22
Abstract :
The study on Transductive Support Vector Machine (TSVM) has made little progress since Vapnik put forth the concept in the late 1990s, as algorithm for TSVM optimization model can not be easily found. Here we try to transform the problem of TSVM optimization into an unconstrained one before constructing the smooth unconstrained optimization that has a kernel, and on the basis of which to devise a TSVM whose optimization problem is easier to solve to break through the bottleneck in order to deepen the research into TSVM and apply TSVM to network intrusion detection therefore provide a new method for it.
Keywords :
optimisation; security of data; smoothing methods; support vector machines; TSVM optimization model; network intrusion detection; smooth unconstrained optimization; transductive support vector machine; Data mining; Intrusion detection; Kernel; Simulated annealing; Support vector machines; Temperature measurement; Network Intrusion Detection; Optimization; Smoothing Function; Transductive Support Vector Machine; Unconstrained;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Software Engineering and Service Sciences (ICSESS), 2010 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-6054-0
Type :
conf
DOI :
10.1109/ICSESS.2010.5552345
Filename :
5552345
Link To Document :
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