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Classifying sybil in MSNs using C4.5
Authors
A Chinchore
F Jiang
G Xu
Publication date
1 January 2016
Publisher
'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Cite
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
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OPUS - University of Technology Sydney
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oai:opus.lib.uts.edu.au:10453/...
Last time updated on 18/10/2019
Deakin Research Online
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oai:dro.deakin.edu.au:DU:30135...
Last time updated on 16/03/2020