A Labeled Data Set For Flow-based Intrusion Detection Download
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.
Proceedings of the 9th IEEE International Workshop on IP Operations and Management (IPOM 09)
|Submitted On:||27 Feb 2010|
|Submitted By:||JosepMĒ Tomās Sanahuja (JosepM)|
|File Date:||27 Feb 2010|
|Author:||A. Sperotto, R. Sadre, F. van Vliet, A. Pras|