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National Repository of Grey Literature 2 records found  Search took 0.00 seconds. 
Extending NetFlow Records for Increasing Encrypted Traffic Classification Capabilities
 Šuhaj, Peter ; Jeřábek, Kamil (referee) ; Holkovič, Martin (advisor)
Master's thesis deals with selection of attributes proper for classification of encrypted traffic, with the extension of NetFlow entries with these attributes and with creating a tool for classify encrypted TLS traffic. The following attributes were selected: size of packets, inter-packet arrival times, number of packets in flow and size of the flow. Selection of attributes was followed by design of extending NetFlow records with these attributes for classifying encrypted traffic. Extension of records was implemented in language C for exporter of the company Flowmon Networks a.s.. Classifier for collector was implemented in language Python. Classifier is based on a model, for which training data were needed. The exporter contains the classifying algorithm too, the place of the classification can be set. The implementation was followed by creation of testing data and evaluation of the accuracy. The speed of the classifier was tested too. In the best case scenario 47% accuracy was achieved.
Detailed record
Extending NetFlow Records for Increasing Encrypted Traffic Classification Capabilities
 Šuhaj, Peter ; Jeřábek, Kamil (referee) ; Holkovič, Martin (advisor)
Master's thesis deals with selection of attributes proper for classification of encrypted traffic, with the extension of NetFlow entries with these attributes and with creating a tool for classify encrypted TLS traffic. The following attributes were selected: size of packets, inter-packet arrival times, number of packets in flow and size of the flow. Selection of attributes was followed by design of extending NetFlow records with these attributes for classifying encrypted traffic. Extension of records was implemented in language C for exporter of the company Flowmon Networks a.s.. Classifier for collector was implemented in language Python. Classifier is based on a model, for which training data were needed. The exporter contains the classifying algorithm too, the place of the classification can be set. The implementation was followed by creation of testing data and evaluation of the accuracy. The speed of the classifier was tested too. In the best case scenario 47% accuracy was achieved.
Detailed record

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