Classifying sybil in MSNs using C4.5

Chinchore, A; Xu, G; Jiang, F

Classifying sybil in MSNs using C4.5

Chinchore, A Xu, G

Jiang, F

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Publication Type:: Conference Proceeding
Citation:: IEEE/ACM BESC 2016 - Proceedings of 2016 International Conference on Behavioral, Economic, Socio - Cultural Computing, 2017
Issue Date:: 2017-01-03

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Field	Value	Language
dc.contributor.author	Chinchore, A	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.contributor.author	Jiang, F	en_US
dc.date.issued	2017-01-03	en_US
dc.identifier.citation	IEEE/ACM BESC 2016 - Proceedings of 2016 International Conference on Behavioral, Economic, Socio - Cultural Computing, 2017	en_US
dc.identifier.isbn	9781509061648	en_US
dc.identifier.uri	http://hdl.handle.net/10453/103825
dc.description.abstract	© 2016 IEEE. Sybil detection is an important task in cyber security research. Over past years, many data mining algorithms have been adopted to fulfill such task. Using classification and regression for sybil detection is a very challenging task. Despite of existing research made toward modeling classification for sybil detection and prediction, this research has proposed new solution on how sybil activity could be tracked to address this challenging issue. Prediction of sybil behaviour has been demonstrated by analysing the graph-based classification and regression techniques, using decision trees and described dependencies across different methods. Calculated gain and maxGain helped to trace some sybil users in the datasets.	en_US
dc.relation.ispartof	IEEE/ACM BESC 2016 - Proceedings of 2016 International Conference on Behavioral, Economic, Socio - Cultural Computing	en_US
dc.relation.isbasedon	10.1109/BESC.2016.7804499	en_US
dc.title	Classifying sybil in MSNs using C4.5	en_US
dc.type	Conference Proceeding
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Resources)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Provost
pubs.organisational-group	/University of Technology Sydney/Provost/Jumbunna
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. Sybil detection is an important task in cyber security research. Over past years, many data mining algorithms have been adopted to fulfill such task. Using classification and regression for sybil detection is a very challenging task. Despite of existing research made toward modeling classification for sybil detection and prediction, this research has proposed new solution on how sybil activity could be tracked to address this challenging issue. Prediction of sybil behaviour has been demonstrated by analysing the graph-based classification and regression techniques, using decision trees and described dependencies across different methods. Calculated gain and maxGain helped to trace some sybil users in the datasets.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/103825