research-article

Using an improved C4.5 for imbalanced dataset of intrusion

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Shi-fu ChenAuthors Info & Claims

PST '06: Proceedings of the 2006 International Conference on Privacy, Security and Trust: Bridge the Gap Between PST Technologies and Business Services

Article No.: 67, Pages 1 - 4

https://doi.org/10.1145/1501434.1501513

Published: 30 October 2006 Publication History

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Abstract

The imbalance of dataset will directly affect the precision of classifier. PC4.5, an improved C4.5 algorithm is proposed. The experiments in MIT dataset indicate that PC4.5 is effective on imbalanced dataset and the scale of the decision tree could be reduced.

References

[1]

Ross J Quinlan. C4.5: Programs for machine learning{M}. American: Morgan Kaufman Publishers, 1993.

Digital Library

Google Scholar

[2]

Zhang Qi-rui, Zhang Lin, Dong Shou-bin. The influence of class distribution on Text Categorization. Journal of Tsinghua University (Science and Technology). 2005 (45): 1802--1805

Google Scholar

[3]

Witten IH, Frank E. Data Ming: Practical Machine Learning Tools and Techniques with Java Implementations. Seattle: Morgan Kaufimann Publishers 2000. 265--314.

Digital Library

Google Scholar

[4]

Chris Drummond & Robert C. Holte. C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling. in proceeding of Learning from Imbalanced Datasets II, ICML, Washington DC, 2003.

Google Scholar

[5]

Prati, R. C., Batista, G. E. A. P. A., and Monard, M. C. Class Imbalances versus Class Overlapping: an Analysis of a Learning System Behavior. In MICAI (2004), pp. 312--321. LNAI 2972.

Crossref

Google Scholar

[6]

A. Kolcz, A. Chowdhury and J. Alspector, "Data Duplication: An Imbalance Problem?," ICML'2003 Workshop on Learning from Imbalanced Datasets, Washington, DC, USA, 2003

Google Scholar

Cited By

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Engly ALarsen AMeng W(2020)Evaluation of Anomaly-Based Intrusion Detection with Combined Imbalance Correction and Feature SelectionNetwork and System Security10.1007/978-3-030-65745-1_16(277-291)Online publication date: 19-Dec-2020
https://doi.org/10.1007/978-3-030-65745-1_16
Wang FMa YJin YJiang YWang Y(2018)RETRACTED ARTICLEMultimedia Tools and Applications10.1007/s11042-017-5057-377:3(3245-3260)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s11042-017-5057-3
Ni XHe DChan SAhmad F(2016)Network Anomaly Detection Using Unsupervised Feature Selection and Density Peak ClusteringApplied Cryptography and Network Security10.1007/978-3-319-39555-5_12(212-227)Online publication date: 9-Jun-2016
https://doi.org/10.1007/978-3-319-39555-5_12
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Information & Contributors

Information

Published In

PST '06: Proceedings of the 2006 International Conference on Privacy, Security and Trust: Bridge the Gap Between PST Technologies and Business Services

October 2006

389 pages

ISBN:1595936041

DOI:10.1145/1501434

General Chair:
Greg Sprague
National Research Council, Fredericton, New Brunswick, Canada
,
Program Chairs:
Bernadette Schell
UOIT, Oshawa, Ontario, Canada
,
Wilfred Fong
UOIT, Oshawa, Ontario, Canada

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSAC: ACM Special Interest Group on Security, Audit, and Control

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 October 2006

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Funding Sources

National Natural Science Foundation of China
Society Development Plan of Jiangsu Province
National Natural Science Foundation of Jiangsu Province

Conference

PST06

PST06: International Conference on Privacy, Security and Trust

October 30 - November 1, 2006

Ontario, Markham, Canada

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Cited By

View all

Engly ALarsen AMeng W(2020)Evaluation of Anomaly-Based Intrusion Detection with Combined Imbalance Correction and Feature SelectionNetwork and System Security10.1007/978-3-030-65745-1_16(277-291)Online publication date: 19-Dec-2020
https://doi.org/10.1007/978-3-030-65745-1_16
Wang FMa YJin YJiang YWang Y(2018)RETRACTED ARTICLEMultimedia Tools and Applications10.1007/s11042-017-5057-377:3(3245-3260)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s11042-017-5057-3
Ni XHe DChan SAhmad F(2016)Network Anomaly Detection Using Unsupervised Feature Selection and Density Peak ClusteringApplied Cryptography and Network Security10.1007/978-3-319-39555-5_12(212-227)Online publication date: 9-Jun-2016
https://doi.org/10.1007/978-3-319-39555-5_12
Adam AShapiai MIbrahim ZKhalid M(2011)Artificial neural network — Naïve bayes fusion for solving classification problem of imbalanced dataset2011 Fourth International Conference on Modeling, Simulation and Applied Optimization10.1109/ICMSAO.2011.5775584(1-5)Online publication date: Apr-2011
https://doi.org/10.1109/ICMSAO.2011.5775584
Zhang JZulkernine MHaque A(2008)Random-Forests-Based Network Intrusion Detection SystemsIEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews10.1109/TSMCC.2008.92387638:5(649-659)Online publication date: 1-Sep-2008
https://dl.acm.org/doi/10.1109/TSMCC.2008.923876

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