Classifying sybil in MSNs using C4.5

Anand Chinchore, Guandong Xu, F. Jiang
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引用次数: 2

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.
使用C4.5对msn中的符号进行分类
网络攻击检测是网络安全研究中的一项重要任务。在过去的几年里,许多数据挖掘算法被用来完成这一任务。使用分类和回归进行符号检测是一项非常具有挑战性的任务。尽管已有的研究是针对女妖检测和预测的建模分类,但本研究提出了如何跟踪女妖活动的新解决方案,以解决这一具有挑战性的问题。通过分析基于图的分类和回归技术,使用决策树和不同方法间描述的依赖关系,证明了sybil行为的预测。计算增益和maxGain帮助跟踪数据集中的一些符号用户。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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