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A Small-time Scale Netflow-based Anomaly Traffic Detecting Method Using MapReduce

원문정보

Wang Jin-Song, Zhang Long, Shi Kai, Zhang Hong-hao

보안공학연구지원센터(IJSIA) International Journal of Security and Its Applications Vol.8 No.2 2014.03 pp.231-242
피인용수 : 0건 (자료제공 : 네이버학술정보)

초록

영어

Anomaly traffic detecting using Netflow data is one of important problems in the field of network security. In this paper, we proposed an approach using MapReduce model, which was realized by means of the entropy observation and DFN (Distinct feature number) distribution deviations of traffic features under anomalies at small time scales. The MapReduce was used to deal with huge amounts of data with the aid of computer cluster processing. Experimental results show the effectiveness of the proposed approach.

목차

Abstract
 1. Introduction
 2. Formatting your Paper
 3. Implementation of the Anomaly Traffic Detecting Method
  3.1. The Network Traffic Features in Anomaly Traffic Detection
  3.2. Implementation using MapReduce
 4. Simulation
 5. Conclusion
 Acknowledgement
 References

키워드

저자정보

  • Wang Jin-Song School of Computer and Communication Engineering, Tianjin University of Technology, Tianjin 300384
  • Zhang Long School of Computer and Communication Engineering, Tianjin University of Technology, Tianjin 300384
  • Shi Kai School of Computer and Communication Engineering, Tianjin University of Technology, Tianjin 300384
  • Zhang Hong-hao School of Computer and Communication Engineering, Tianjin University of Technology, Tianjin 300384

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자료제공 : 네이버학술정보

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