Classifying sybil in MSNs using C4.5

Abstract

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.

DOI: 10.1109/BESC.2016.7804499

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Cite this paper

@article{Chinchore2016ClassifyingSI, title={Classifying sybil in MSNs using C4.5}, author={Anand Chinchore and Guandong Xu and Frank Jiang}, journal={2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)}, year={2016}, pages={1-6} }