Performance Analysis of Data Mining Approaches in Intrusion Detection

Author

Amudha, P. ; Rauf, H. Abdul

Author_Institution

Dept. of Comput. Sci. & Eng., Avinashilingam Univ. for Women, Coimbatore, India

fYear

2011

fDate

20-22 July 2011

Firstpage

1

Lastpage

6

Abstract

Intruder is one of the most publicized threats to security. In recent years, intrusion detection has emerged as an important technique for network security. Data mining techniques have been applied as a new approach for intrusion detection. The quality of the feature selection methods is one of the important factors that affect the effectiveness of Intrusion Detection system (IDS). This paper evaluates the performance of data mining classification algorithms namely J48, Naive Bayes, NBTree and Random Forest using KDD CUP´99 dataset and focuses on Correlation Feature Selection (CFS) measure. The results show that NBTree and Random Forest outperforms other two algorithms in terms of predictive accuracy and detection rate.

Keywords

Bayes methods; Internet; computer network security; data mining; J48; NBTree; correlation feature selection measure; data mining approach; data mining classification algorithms; feature selection methods; intrusion detection system; naive Bayes; network security; random forest; Accuracy; Classification algorithms; Correlation; Data mining; Decision trees; Intrusion detection; Probes;

fLanguage

English

Publisher

ieee

Conference_Titel

Process Automation, Control and Computing (PACC), 2011 International Conference on

Conference_Location

Coimbatore

Print_ISBN

978-1-61284-765-8

Type

conf

DOI

10.1109/PACC.2011.5978878

Filename

5978878