Detecting Network Anomalies in Mixed-Attribute Data Sets

Author

Tran, Khoi-Nguyen ; Jin, Huidong

Author_Institution

Sch. of Comput. Sci., Australian Nat. Univ., Canberra, ACT, Australia

fYear

2010

fDate

9-10 Jan. 2010

Firstpage

383

Lastpage

386

Abstract

Detecting network anomalies is important part of intrusion detection systems that have been developed with great successes on homogeneous data. There have been successes with mixed-attribute data using various techniques, however, few of them exist for using mixed-attribute data without further manipulation or consideration of dependencies among the different types of attributes. We propose in this paper a fusion of decision tree and Gaussian mixture model (GMM) to detect anomalies in mixed-attribute data sets. Evaluation experiments were performed on the popular KDDCup 1999 data set using C4.5 decision tree, GMM and the fusion of C4.5 and GMM.

Keywords

Gaussian processes; decision trees; security of data; C4.5 decision tree; GMM; Gaussian mixture model; KDDCup 1999 data set; decision tree; homogeneous data; intrusion detection systems; mixed-attribute data sets; network anomalies detection; Australia; Computer science; Data analysis; Data mining; Databases; Decision trees; Detectors; Intrusion detection; Performance evaluation; Predictive models; Anomaly detection; C4.5 decision tree; Gaussian mixture model;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge Discovery and Data Mining, 2010. WKDD '10. Third International Conference on

Conference_Location

Phuket

Print_ISBN

978-1-4244-5397-9

Electronic_ISBN

978-1-4244-5398-6

Type

conf

DOI

10.1109/WKDD.2010.96

Filename

5432576