An Intrusion Detection Method Based on Outlier Ensemble Detection

Author

Huang, Bin ; Li, Wen-fang ; Chen, De-li ; Shi, Liang

Author_Institution

Electron. Inf. Eng. Dept., Putian Univ., Putian

Volume

2

fYear

2009

fDate

25-26 April 2009

Firstpage

600

Lastpage

603

Abstract

In this paper, we try to bring the concept of Ensemble into Outlier Detection. Two Outlier mining algorithms are ensembled: one based on similar coefficient sum and the other based on kernel density. An anomaly detection approach based on voting mechanism is proposed and applied into intrusion detection. We convert the character feature into numerical value by code mapping and use principal Components Analysis (PCA) to reduce dimension. We apply this technique on KDD99 data set and get satisfactory results.

Keywords

principal component analysis; security of data; KDD99 data set; PCA; anomaly detection approach; intrusion detection method; kernel density; outlier ensemble detection; outlier mining algorithm; principal components analysis; voting mechanism; Algorithm design and analysis; Clustering algorithms; Computer networks; Data analysis; Data mining; Information security; Intrusion detection; Kernel; Voting; Wireless communication; Ensemble; Intrusion Detection; Outlier Mining; Voting Mechanism;

fLanguage

English

Publisher

ieee

Conference_Titel

Networks Security, Wireless Communications and Trusted Computing, 2009. NSWCTC '09. International Conference on

Conference_Location

Wuhan, Hubei

Print_ISBN

978-1-4244-4223-2

Type

conf

DOI

10.1109/NSWCTC.2009.292

Filename

4908540