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Information Fusion for Classification of Network Devices
 Sedláček, Ondřej ; Tisovčík, Peter (referee) ; Žádník, Martin (advisor)
This work is focused on solving information fusion when dealing with multiple data sources in computer network monitoring. A solution built on the concept of classification rules configured by experts is presented. Configuration is simplified using a designated configuration language interpreted by the solution. The classification rules enable coverage of diverse types of data. The result is given as a label from specified taxonomy. Using a taxonomy maintains the different levels of detail between the data sources, even in the output label. The solution also uses the Dempster-Schafer theory for merging labels from different sources into a single output label. Results of experiments show that information fusion in this context does increase the accuracy of device classification. A process of rule optimization was developed based on testing and experiments with a dataset from a real network. The accuracy was increased by 19 % compared to the original solution using this process.
Detailed record
Information Fusion for Classification of Network Devices
 Sedláček, Ondřej ; Tisovčík, Peter (referee) ; Žádník, Martin (advisor)
This work is focused on solving information fusion when dealing with multiple data sources in computer network monitoring. A solution built on the concept of classification rules configured by experts is presented. Configuration is simplified using a designated configuration language interpreted by the solution. The classification rules enable coverage of diverse types of data. The result is given as a label from specified taxonomy. Using a taxonomy maintains the different levels of detail between the data sources, even in the output label. The solution also uses the Dempster-Schafer theory for merging labels from different sources into a single output label. Results of experiments show that information fusion in this context does increase the accuracy of device classification. A process of rule optimization was developed based on testing and experiments with a dataset from a real network. The accuracy was increased by 19 % compared to the original solution using this process.
Detailed record

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