DocumentCode :
2387356
Title :
Intrusion detection using data mining techniques
Author :
Ektefa, Mohammadreza ; Memar, Sara ; Sidi, Fatimah ; Affendey, Lilly Suriani
Author_Institution :
Dept. of IS, UPM, Serdang, Malaysia
fYear :
2010
fDate :
17-18 March 2010
Firstpage :
200
Lastpage :
203
Abstract :
As the network dramatically extended, security considered as major issue in networks. Internet attacks are increasing, and there have been various attack methods, consequently. Intrusion detection systems have been used along with the data mining techniques to detect intrusions. In this work we aim to use data mining techniques including classification tree and support vector machines for intrusion detection. As results indicate, C4.5 algorithm is better than SVM in detecting network intrusions and false alarm rate in KDD CUP 99 dataset.
Keywords :
data mining; security of data; support vector machines; C4.5 algorithm; Internet attacks; data mining; intrusion detection; support vector machines; Application software; Classification tree analysis; Data mining; Data security; IP networks; Internet; Intrusion detection; Support vector machine classification; Support vector machines; Telecommunication traffic; Classification tree; Data Mining; Internet attack; Intrusion Detection Systems (IDS); Support Vector Machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Retrieval & Knowledge Management, (CAMP), 2010 International Conference on
Conference_Location :
Shah Alam, Selangor
Print_ISBN :
978-1-4244-5650-5
Type :
conf
DOI :
10.1109/INFRKM.2010.5466919
Filename :
5466919
Link To Document :
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