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Sperotto, A. and Sadre, R. and van Vliet, D.F. and Pras, A.
(2009)
A Labeled Data Set For Flow-based Intrusion Detection.
In: IP Operations and Management, Proceedings of the 9th IEEE Intenational Workshop IPOM 2009, October 29-30, 2009, Venice, Italy.
pp. 39-50.
Lecture Notes in Computer Science 5843/2009.
Springer Verlag.
ISSN 0302-9743
ISBN 978-3-642-04967-5
Full text available as:
Official URL: http://dx.doi.org/10.1007/978-3-642-04968-2_4  AbstractFlow-based intrusion detection has recently become a promising security mechanism in high speed networks (1-10 Gbps). Despite the richness in contributions in this field, benchmarking of flow-based IDS is still an open issue. In this paper, we propose the first publicly available, labeled data set for flow-based intrusion detection. The data set aims to be realistic, i.e., representative of real traffic and complete from a labeling perspective. Our goal is to provide such enriched data set for tuning, training and evaluating ID systems. Our setup is based on a honeypot running widely deployed services and directly connected to the Internet, ensuring attack-exposure. The final data set consists of 14.2M flows and more than 98% of them has been labeled.
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