Abstract
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.
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Kim, S., Reddy, A., Vannucci, M.: Detecting Traffic Anomalies through Aggregate Analysis of Packet Header Data. In: Networking 2004, Athens (2004)
Lakhina, A., Crovella, M., Diot, C.: Mining Anomalies Using Traffic Feature Distributions. In: ACM SIGCOMM, Philadelphia (2005)
Farraposo, S., Owezarski, P., Monteiro, E.: A Multi-Scale Tomographic Algorithm for Detecting and Classifying Traffic Anomalies. In: IEEE ICC 2007, Glasgow (2007)
Cormode, G., Muthukrishnan, S.: What’s New: Finding Significant Differences in Network Data Streams. In: IEEE/ACM Transactions on Networking, vol. 13 (2005)
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© 2007 IFIP International Federation for Information Processing
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Farraposo, S., Owezarski, P., Monteiro, E. (2007). NADA – Network Anomaly Detection Algorithm. In: Clemm, A., Granville, L.Z., Stadler, R. (eds) Managing Virtualization of Networks and Services. DSOM 2007. Lecture Notes in Computer Science, vol 4785. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75694-1_18
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DOI: https://doi.org/10.1007/978-3-540-75694-1_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75693-4
Online ISBN: 978-3-540-75694-1
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