Detail publikace

Network Supervision via Protocol Identification in the Network

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"
}