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An Improvement for Fast-Flux Service Networks Detection Based on Data Mining Techniques

  • Ziniu Chen
  • Jian Wang
  • Yujian Zhou
  • Chunping Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6743)

Abstract

Fast-flux is a kind of DNS technique used by botnets to hide the actual location of malicious servers. It is considered as an emerging threat for information security. In this paper, we propose an approach to detect the fast-flux service network (FFSN) using data mining techniques. Furthermore, we use the resampling technique to solve imbalanced classification problem with respect to FFSNs detection. Experiment results in the real datasets show that our approach improves the detective precision and effectiveness compared with existing researches.

Keywords

Random Forest Rate Flux Data Mining Technique Detective Precision Malicious Server 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ziniu Chen
    • 1
  • Jian Wang
    • 1
  • Yujian Zhou
    • 2
  • Chunping Li
    • 1
  1. 1.Data Mining Group, School of SoftwareTsinghua UniversityBeijingChina
  2. 2.MOST Information CenterBeijingChina

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