Anomaly Detection and Traffic Shaping under Self-Similar Aggregated Traffic in Optical Switched Networks

Research output: Contribution to conferencePaperpeer-review

3 Scopus citations

Abstract

Recent work in traffic analysis has shown that modern network produces traffic streams that are self-similar over several time scales from microseconds to minutes. Simulation studies have demonstrated that self-similarity leads to larger queueing delays and higher drop rates than the Markovian Short Range Dependence (SRD) traffic. At the same time, the dramatic expansion of applications on modern network gives rise to a fundamental challenge for network monitoring and security. Therefore, it is critical to reduce the degree of second order scaling for better network performance and detect traffic anomalies efficiently. In this paper, we propose an approach which can capture the traffic anomalies and decrease the degree of Long Range Dependence at the conjunction of the optical packet switching backbone network. In this method, a traffic shaping technique is proposed and a reference model is generated based on the well-behaving traffic for anomaly detection. Further, we apply the compensation bursty parameter for smoothing the deviation error caused by burstiness difference existing in the traffic data sets. The simulation results show that our work can decrease the degree of self-similarity and detect the anomaly-behaving traffic efficiently.

Original languageEnglish (US)
Pages378-381
Number of pages4
StatePublished - 2003
Event2003 International Conference on Communication Technology, ICCT 2003 - Beijing, China
Duration: Apr 9 2003Apr 11 2003

Other

Other2003 International Conference on Communication Technology, ICCT 2003
CountryChina
CityBeijing
Period4/9/034/11/03

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Keywords

  • Hurst parameter
  • Network security
  • Self-similar
  • Traffic anomalies
  • Traffic shaping

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