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• W2924689635 abstract "Labeled data sets are necessary to train and evaluate anomaly-based network intrusion detection systems. This work provides a focused literature survey of data sets for network-based intrusion detection and describes the underlying packet- and flow-based network data in detail. The paper identifies 15 different properties to assess the suitability of individual data sets for specific evaluation scenarios. These properties cover a wide range of criteria and are grouped into five categories such as data volume or recording environment for offering a structured search. Based on these properties, a comprehensive overview of existing data sets is given. This overview also highlights the peculiarities of each data set. Furthermore, this work briefly touches upon other sources for network-based data such as traffic generators and traffic repositories. Finally, we discuss our observations and provide some recommendations for the use and creation of network-based data sets." @default.
• W2924689635 created "2019-04-01" @default.
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• W2924689635 date "2019-09-01" @default.
• W2924689635 modified "2023-10-17" @default.
• W2924689635 title "A survey of network-based intrusion detection data sets" @default.
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• W2924689635 doi "https://doi.org/10.1016/j.cose.2019.06.005" @default.
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