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  • 标题:Botnet analysis using ensemble classifier
  • 本地全文:下载
  • 作者:Anchit Bijalwan ; Anchit Bijalwan ; Nanak Chand
  • 期刊名称:Perspectives in Science
  • 印刷版ISSN:2213-0209
  • 电子版ISSN:2213-0209
  • 出版年度:2016
  • 卷号:8
  • 页码:502-504
  • DOI:10.1016/j.pisc.2016.05.008
  • 语种:English
  • 出版社:Elsevier
  • 摘要:Summary This paper analyses the botnet traffic using Ensemble of classifier algorithm to find out bot evidence. We used ISCX dataset for training and testing purpose. We extracted the features of both training and testing datasets. After extracting the features of this dataset, we bifurcated these features into two classes, normal traffic and botnet traffic and provide labelling. Thereafter using modern data mining tool, we have applied ensemble of classifier algorithm. Our experimental results show that the performance for finding bot evidence using ensemble of classifiers is better than single classifier. Ensemble based classifiers perform better than single classifier by either combining powers of multiple algorithms or introducing diversification to the same classifier by varying input in bot analysis. Our results are showing that by using voting method of ensemble based classifier accuracy is increased up to 96.41% from 93.37%.
  • 关键词:Botnet; Ensemble of classifier; Machine learning;
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