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Classifying sybil in MSNs using C4.5

conference contribution
posted on 2016-01-01, 00:00 authored by A Chinchore, G Xu, Frank JiangFrank Jiang
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.

History

Pagination

1-6

Location

Durham, N.C.

Start date

2016-11-11

End date

2016-11-13

ISBN-13

9781509061648

Language

eng

Publication classification

E1.1 Full written paper - refereed

Editor/Contributor(s)

[unknown]

Title of proceedings

BESC 2016 - Proceedings of the 2016 International Conference on Behavioral, Economic, Socio - Cultural Computing

Event

Behavioral, Economic and Socio-cultural Computing. International Conference (2016 : Durham, N.C.)

Publisher

IEEE

Place of publication

Piscataway, N.J.

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