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File Title:  A Labeled Data Set For Flow-based Intrusion Detection   download_trans.gifDownload
 
 
Description: 
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
Downloads:  7
 
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