Detecting network anomalies in mixed-attribute data sets

Khoi Nguyen Tran; Huidong Jin

doi:10.1109/WKDD.2010.96

Detecting network anomalies in mixed-attribute data sets

Khoi Nguyen Tran^*, Huidong Jin

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

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.

Original language	English
Title of host publication	3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010
Pages	383-386
Number of pages	4
DOIs	https://doi.org/10.1109/WKDD.2010.96
Publication status	Published - 2010
Event	3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010 - Phuket, Thailand Duration: 9 Jan 2010 → 10 Jan 2010

Publication series

Name	3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010

Conference

Conference	3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010
Country/Territory	Thailand
City	Phuket
Period	9/01/10 → 10/01/10

Access to Document

10.1109/WKDD.2010.96

Cite this

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title = "Detecting network anomalies in mixed-attribute data sets",

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 = "Anomaly detection, C4.5 decision tree, Gaussian mixture model",

author = "Tran, {Khoi Nguyen} and Huidong Jin",

year = "2010",

doi = "10.1109/WKDD.2010.96",

language = "English",

isbn = "9780769539232",

series = "3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010",

pages = "383--386",

booktitle = "3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010",

note = "3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010 ; Conference date: 09-01-2010 Through 10-01-2010",

}

Tran, KN & Jin, H 2010, Detecting network anomalies in mixed-attribute data sets. in 3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010., 5432576, 3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010, pp. 383-386, 3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010, Phuket, Thailand, 9/01/10. https://doi.org/10.1109/WKDD.2010.96

Detecting network anomalies in mixed-attribute data sets. / Tran, Khoi Nguyen; Jin, Huidong.
3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010. 2010. p. 383-386 5432576 (3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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.

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M3 - Conference contribution

SN - 9780769539232

T3 - 3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010

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Detecting network anomalies in mixed-attribute data sets

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