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HOLASOVÁ, E. KUCHAŘ, K. FUJDIAK, R.
Originální název
Network Supervision via Protocol Identification in the Network
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
This paper is focused on a comparison of ML (Machine Learning) and DNN (Deep Neural Network) techniques in protocol recognition to support network supervision for further proper handling, e.g., detection of a security incident. The DNN approach uses 11 layers and the ML approach is consisting of 28 mutually different predictive models. Both techniques were performed/compared on a freely accessible dataset containing browsing pcap files for further comparison, e.g., with other approaches. The predictive multiclass models were trained (fitted) to be capable of detecting five network protocols. Both approaches were compared by the achieved accuracy (based on testing and validating data), learning time, and predicting the time point of view. Using the ML approach, we were able to recognize the protocol with an accuracy of 1 and using DNN with an accuracy of 0.97.
Klíčová slova
IT protocols, neural networks, machine learning, protocol recognition
Autoři
HOLASOVÁ, E.; KUCHAŘ, K.; FUJDIAK, R.
Vydáno
26. 4. 2022
Nakladatel
Brno University of Technology, Faculty of Electrical Engineering and Communication
Místo
Brno
ISBN
978-80-214-6029-4
Kniha
Proceedings I of the 28th Conference STUDENT EEICT 2022 General papers
Edice
1
Strany od
470
Strany do
474
Strany počet
5
URL
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1_v2.pdf
BibTex
@inproceedings{BUT177734, author="Eva {Holasová} and Karel {Kuchař} and Radek {Fujdiak}", title="Network Supervision via Protocol Identification in the Network", booktitle="Proceedings I of the 28th Conference STUDENT EEICT 2022 General papers", year="2022", series="1", pages="470--474", publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication", address="Brno", isbn="978-80-214-6029-4", url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1_v2.pdf" }