A Labeled Data Set For Flow-based Intrusion Detection

Sperotto, Anna and Sadre, Ramin and Vliet van, Frank and Pras, Aiko (2009) A Labeled Data Set For Flow-based Intrusion Detection. In: IP Operations and Management. Lecture Notes in Computer Science 5843 . UNSPECIFIED, Berlin, pp. 39-50. ISBN 9783642049675

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Abstract:	Flow-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.
Item Type:	Book Section
Copyright:	© 2009 Springer
Faculty:	Electrical Engineering, Mathematics and Computer Science (EEMCS)
Research Group:	Design and Analysis of Communication Systems (DACS)
Link to this item:	http://purl.utwente.nl/publications/68310
Official URL:	http://dx.doi.org/10.1007/978-3-642-04968-2_4
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