DocumentCode :
1634174
Title :
An intrusion detection method based on rough set and SVM algorithm
Author :
Hong, Peng ; Zhang, Dongna ; Wu, Tiefeng
Author_Institution :
Sch. of Comput. & Math.-Phys. Sci., Xihua Univ., Chengdu, China
Volume :
2
fYear :
2004
Firstpage :
1127
Abstract :
This paper proposes an intrusion detection method that combines rough sets and SVM algorithm. By virtue of the ability rough sets have to decrease the amount of data and get rid of redundancy, the method can reduce the amount of training data and overcome the SVM defect of slow running speed when processing large datasets. At the same time, by the aid of the SVM algorithm the method can classify the core of the property set to have extensiveness and high identification rate, and avoid disturbances. Experimental results show this method is better than other methods reported in the literature in terms of detection resolution.
Keywords :
authorisation; computer network management; data reduction; rough set theory; support vector machines; SVM algorithm; data reduction; identification rate; intrusion detection method; rough sets; Computer networks; Computer security; Data security; Intrusion detection; Learning systems; Machine learning algorithms; Redundancy; Support vector machine classification; Support vector machines; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communications, Circuits and Systems, 2004. ICCCAS 2004. 2004 International Conference on
Print_ISBN :
0-7803-8647-7
Type :
conf
DOI :
10.1109/ICCCAS.2004.1346374
Filename :
1346374
Link To Document :
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