Article

Intrusion Detection Combining Multiple Decision Trees by Fuzzy logic

Authors:

Jun-feng Tian,

Yue Fu,

Ying Xu,

Jian-ling WangAuthors Info & Claims

PDCAT '05: Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies

Pages 256 - 258

https://doi.org/10.1109/PDCAT.2005.157

Published: 05 December 2005 Publication History

Publisher Site

Abstract

In order to improve detection performance of data mining-based intrusion detection system, this paper presents a method of combining multiple decision trees based on fuzzy logic, especially the fuzzy integral. The main idea of this method is to divide a great large dataset into several sub-datasets, mine on sub-datasets separately to construct different sub-decision trees, detect TCP data by different sub-decision trees, and then nonlinearly combine the results from multiple sub-decision trees by fuzzy integral. The experiment results show that this technique is superior to individual decision trees for intrusion detection in terms of classification accuracy.

Cited By

View all

Alsharafat W(2018)Secure software engineering requirements in cloud environment by using anticipating learning classifier systemInternational Journal of Internet Technology and Secured Transactions10.1504/IJITST.2015.0739146:1(1-8)Online publication date: 13-Dec-2018
https://dl.acm.org/doi/10.1504/IJITST.2015.073914
Aldwairi MKhamayseh YAl-Masri M(2015)Application of artificial bee colony for intrusion detection systemsSecurity and Communication Networks10.1002/sec.5888:16(2730-2740)Online publication date: 10-Nov-2015
https://dl.acm.org/doi/10.1002/sec.588
Abadeh MMohamadi HHabibi J(2011)Design and analysis of genetic fuzzy systems for intrusion detection in computer networksExpert Systems with Applications: An International Journal10.1016/j.eswa.2010.12.00638:6(7067-7075)Online publication date: 1-Jun-2011
https://dl.acm.org/doi/10.1016/j.eswa.2010.12.006
Show More Cited By

Index Terms

Intrusion Detection Combining Multiple Decision Trees by Fuzzy logic

Recommendations

Overview of Type-2 Fuzzy Logic Systems

Fuzzy set theory has been proposed as a means for modeling the vagueness in complex systems. Fuzzy systems usually employ type-1 fuzzy sets, representing uncertainty by numbers in the range [0, 1]. Despite commercial success of fuzzy logic, a type-1 ...
The development of fuzzy decision trees in the framework of Axiomatic Fuzzy Set logic
Abstract
Decision trees are one among interesting and commonly encountered architectures used for learning, reasoning and organization of datasets. This study being positioned in the realm of decision trees is aimed at two main objectives. ...
A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems

This paper presents a comparative study of type-2 fuzzy logic systems with respect to interval type-2 and type-1 fuzzy logic systems to show the efficiency and performance of a generalized type-2 fuzzy logic controller (GT2FLC). We used different types ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

PDCAT '05: Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies

December 2005

1086 pages

ISBN:0769524052

Publisher

IEEE Computer Society

United States

Publication History

Published: 05 December 2005

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 16 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Alsharafat W(2018)Secure software engineering requirements in cloud environment by using anticipating learning classifier systemInternational Journal of Internet Technology and Secured Transactions10.1504/IJITST.2015.0739146:1(1-8)Online publication date: 13-Dec-2018
https://dl.acm.org/doi/10.1504/IJITST.2015.073914
Aldwairi MKhamayseh YAl-Masri M(2015)Application of artificial bee colony for intrusion detection systemsSecurity and Communication Networks10.1002/sec.5888:16(2730-2740)Online publication date: 10-Nov-2015
https://dl.acm.org/doi/10.1002/sec.588
Abadeh MMohamadi HHabibi J(2011)Design and analysis of genetic fuzzy systems for intrusion detection in computer networksExpert Systems with Applications: An International Journal10.1016/j.eswa.2010.12.00638:6(7067-7075)Online publication date: 1-Jun-2011
https://dl.acm.org/doi/10.1016/j.eswa.2010.12.006
Wu SBanzhaf W(2010)ReviewApplied Soft Computing10.1016/j.asoc.2009.06.01910:1(1-35)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.1016/j.asoc.2009.06.019

Abstract

Cited By

Index Terms

Recommendations

Overview of Type-2 Fuzzy Logic Systems

The development of fuzzy decision trees in the framework of Axiomatic Fuzzy Set logic

A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems

Comments

Information

Published In

Publisher

Publication History

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations