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Erschienen in:
2009 | OriginalPaper | Buchkapitel
A Labeled Data Set for Flow-Based Intrusion Detection
verfasst von : Anna Sperotto, Ramin Sadre, Frank van Vliet, Aiko Pras
Erschienen in: IP Operations and Management
Verlag: Springer Berlin Heidelberg
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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.