DocumentCode :
2868291
Title :
Application of data mining in intrusion detection
Author :
Yun, Li ; Xue-Cheng, Liu ; Feng, Zhu
Author_Institution :
Dept. of Math., TaiShan Coll., Taian, China
Volume :
10
fYear :
2010
fDate :
22-24 Oct. 2010
Abstract :
This paper analyzing the lastest research progress and mian problems existed of IDS, researching advantage of data mining technique applied to IDS, and analyzing disadvantage of IDS based on data mining technique, for the problem of time and space inefficient in intrusion detection based on data mining, and aims at the research of frequential pattern algorithm, inproved frequential pattern algorithm, used two-step growth model instead of one step model to speed up the mining speed, and increase in time features, attribute related and axis attribute to constraint, and the experimental results show the proved algorithm improve the time and space efficiency, decrease the time of scan database and generate less meaningless pattern, heighten the availability for rule.
Keywords :
data mining; security of data; IDS; data mining; frequential pattern algorithm; intrusion detection; one step model; two-step growth model; Fires; frequential pattern; intrusion detection; network security;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Application and System Modeling (ICCASM), 2010 International Conference on
Conference_Location :
Taiyuan
Print_ISBN :
978-1-4244-7235-2
Electronic_ISBN :
978-1-4244-7237-6
Type :
conf
DOI :
10.1109/ICCASM.2010.5622853
Filename :
5622853
Link To Document :
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