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Statistical Network Anomaly Detection: An Experimental Study

  • Christian CallegariEmail author
  • Stefano Giordano
  • Michele Pagano
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 670)

Abstract

The number and impact of attack over the Internet have been continuously increasing in the last years, pushing the focus of many research activities into the development of effective techniques to promptly detect and identify anomalies in the network traffic. In this paper, we propose a performance comparison between two different histogram based anomaly detection methods, which use either the Euclidean distance or the entropy to measure the deviation from the normal behaviour. Such an analysis has been carried out taking into consideration different traffic features.

The experimental results, obtained testing our systems over the publicly available MAWILAb dataset, point out that both the applied method and the chosen descriptor strongly impact the detection performance.

Keywords

Hash Function Shannon Entropy Anomaly Detection Traffic Feature Minkowski Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgment

This work was partially supported by Multitech SeCurity system for intercOnnected space control groUnd staTions (SCOUT), a FP7 EU project.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Christian Callegari
    • 1
    Email author
  • Stefano Giordano
    • 2
  • Michele Pagano
    • 2
  1. 1.RaSS National LaboratoryCNITPisaItaly
  2. 2.Department of Information EngineeringUniversity of PisaPisaItaly

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