Network Anomaly Detection based on Multi-scale Dynamic Characteristics of Traffic

  • Jing Yuan Department of Automation, Tsinghua University Beijing, China 100084
  • Ruixi Yuan Department of Automation, Tsinghua University Beijing, China 100084
  • Xi Chen Department of Automation, Tsinghua University Beijing, China 100084


This paper proposes a novel detection engine, called the Wavelet-Recurrence-Clustering (WRC) detection model, to study the network anomaly detection problem that is widely attractive in Internet security area. The WRC model firstapplies the wavelet transform and recurrence analysis to calculate the multi-scale dynamic characteristics of network traffic, and then identifies network anomalies throughthe clustering algorithm with those dynamic characteristics. The evaluation results on DARPA 1999 dataset indicate that the WRC detection model can effectively improve the detection accuracy with a low false alarm rate.

Author Biography

Jing Yuan, Department of Automation, Tsinghua University Beijing, China 100084
Department of Mathematics and Computer Science


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How to Cite
YUAN, Jing; YUAN, Ruixi; CHEN, Xi. Network Anomaly Detection based on Multi-scale Dynamic Characteristics of Traffic. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 9, n. 1, p. 101-112, jan. 2014. ISSN 1841-9844. Available at: <>. Date accessed: 05 july 2020. doi:


network anomaly detection, multi-scale dynamic characteristics, recurrence analysis, WRC detection model