Effects of sampling and spatio/temporal granularity in traffic monitoring on anomaly detectability

Keisuke Ishibashi*, Ryoichi Kawahara, Tatsuya Mori, Tsuyoshi Kondoh, Shoichiro Asano

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


We quantitatively evaluate how sampling and spatio/ temporal granularity in traffic monitoring affect the detectability of anomalous traffic. Those parameters also affect the monitoring burden, so network operators face a trade-off between the monitoring burden and detectability and need to know which are the optimal paramter values. We derive equations to calculate the false positive ratio and false negative ratio for given values of the sampling rate, granularity, statistics of normal traffic, and volume of anomalies to be detected. Specifically, assuming that the normal traffic has a Gaussian distribution, which is parameterized by its mean and standard deviation, we analyze how sampling and monitoring granularity change these distribution parameters. This analysis is based on observation of the backbone traffic, which exhibits spatially uncorrelated and temporally long-range dependence. Then we derive the equations for detectability. With those equations, we can answer the practical questions that arise in actual network operations: what sampling rate to set to find the given volume of anomaly, or, if the sampling is too high for actual operation, what granularity is optimal to find the anomaly for a given lower limit of sampling rate.

Original languageEnglish
Pages (from-to)466-476
Number of pages11
JournalIEICE Transactions on Communications
Issue number2
Publication statusPublished - 2012 Feb
Externally publishedYes


  • Anomaly detection
  • Hurst parameter
  • Sampling
  • Spatio/temporal granularity

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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