NADA – Network Anomaly Detection Algorithm

  • Sílvia Farraposo
  • Philippe Owezarski
  • Edmundo Monteiro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4785)


This paper deals with a new iterative Network Anomaly Detection Algorithm – NADA, which accomplishes the detection, classification and identification of traffic anomalies. NADA fully provides all information required limiting the extent of anomalies by locating them in time, by classifying them, and identifying their features as, for instance, the source and destination addresses and ports involved. To reach its goal, NADA uses a generic multi-featured algorithm executed at different time scales and at different levels of IP aggregation. Besides that, the NADA approach contributes to the definition of a set of traffic anomaly behavior-based signatures. The use of these signatures makes NADA suitable and efficient to use in a monitoring environment.


Traffic Anomaly Identification Anomaly Signature 


  1. 1.
    Kim, S., Reddy, A., Vannucci, M.: Detecting Traffic Anomalies through Aggregate Analysis of Packet Header Data. In: Networking 2004, Athens (2004)Google Scholar
  2. 2.
    Lakhina, A., Crovella, M., Diot, C.: Mining Anomalies Using Traffic Feature Distributions. In: ACM SIGCOMM, Philadelphia (2005)Google Scholar
  3. 3.
    Farraposo, S., Owezarski, P., Monteiro, E.: A Multi-Scale Tomographic Algorithm for Detecting and Classifying Traffic Anomalies. In: IEEE ICC 2007, Glasgow (2007)Google Scholar
  4. 4.
    Cormode, G., Muthukrishnan, S.: What’s New: Finding Significant Differences in Network Data Streams. In: IEEE/ACM Transactions on Networking, vol. 13 (2005)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2007

Authors and Affiliations

  • Sílvia Farraposo
    • 1
  • Philippe Owezarski
    • 2
  • Edmundo Monteiro
    • 3
  1. 1.School of Technology and Management of Leiria, Alto-Vieiro, Morro do Lena, 2411-901 Leiria, Apartado 4163Portugal
  2. 2.LAAS – CNRS, 7 Avenue du Colonel Roche, 31077 Toulouse, CEDEX 4France
  3. 3.Laboratory of Communications and Telematics, Computer Science Department, Pólo II – Pinhal de Marrocos, 3030-290 CoimbraPortugal

Personalised recommendations