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Detecting Flooding DDoS Under Flash Crowds Based on Mondrian Forest

  • Degang Sun
  • Kun Yang
  • Zhixin Shi
  • Yan Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10251)

Abstract

Flooding Distributed Denial of Service (DDoS) attacks could cause huge damages to Internet, which has much similarity with Flash Crowds (FC). Traditional Machine learning methods usually have a better performance for offline processing, however, they cannot process huge volume data which cannot be loaded in memory at one time and can’t auto-update model in time. In this paper, a streaming detection mechanism based on Online Random Forest-Mondrian Forest is proposed to solve this problem. Firstly, a deep analysis has been done on client’s characteristics of DDoS and FC to find anomaly traffic behaviors in network layer. Based on the analysis, a new feature set has been concluded to describe the client behavior of DDoS and FC. Then a streaming detecting mechanism employed with online Random Forest based on the new feature set has been proposed. To evaluate this method, a comparison with the traditional offline batch process method-Random Forest has been done on two public real-world datasets. The results show that even though this method has a bit lower accuracy around 93% on Test Data, it can be trained like a streaming way which doesn’t need load all data in memory at one time and can update itself automatically with time, which is more applicable for Big Data situations.

Keywords

Flooding DDoS Flash crowds Real-time Detection Online random forest User behavior analysis 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Information EngineeringChinese Academy of SciencesBeijingChina

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