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2010 | OriginalPaper | Buchkapitel

11. Anomaly Detection Approaches for Communication Networks

verfasst von : Marina Thottan, Guanglei Liu, Chuanyi Ji

Erschienen in: Algorithms for Next Generation Networks

Verlag: Springer London

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Abstract

In recent years, network anomaly detection has become an important area for both commercial interests as well as academic research. Applications of anomaly detection typically stem from the perspectives of network monitoring and network security. In network monitoring, a service provider is often interested in capturing such network characteristics as heavy flows, flow size distributions, and the number of distinct flows. In network security, the interest lies in characterizing known or unknown anomalous patterns of an attack or a virus.

In this chapter we review two main approaches to network anomaly detection: streaming algorithms, and machine learning approaches with a focus on unsupervised learning. We discuss the main features of the different approaches and discuss their pros and cons. We conclude the chapter by presenting some open problems in the area of network anomaly detection.

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Titel: Anomaly Detection Approaches for Communication Networks
verfasst von: Marina Thottan
Guanglei Liu
Chuanyi Ji
Verlag: Springer London
Buch: Algorithms for Next Generation Networks
Print ISBN: 978-1-84882-764-6

Electronic ISBN: 978-1-84882-765-3

Copyright-Jahr: 2010
DOI: https://doi.org/10.1007/978-1-84882-765-3_11

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