Multilayer Perceptron Neural Network for Detection of Encrypted VPN Network Traffic

Shane Miller, Kevin Curran, Tom Lunney

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)
61 Downloads (Pure)

Abstract

There has been a growth in popularity of privacy in
the personal computing space and this has influenced the IT
industry. There is more demand for websites to use more secure
and privacy focused technologies such as HTTPS and TLS. This
has had a knock-on effect of increasing the popularity of Virtual
Private Networks (VPNs). There are now more VPN offerings than
ever before and some are exceptionally simple to setup.
Unfortunately, this ease of use means that businesses will have a
need to be able to classify whether an incoming connection to their
network is from an original IP address or if it is being proxied
through a VPN. A method to classify an incoming connection is to
make use of machine learning to learn the general patterns of VPN
and non-VPN traffic in order to build a model capable of
distinguishing between the two in real time. This paper outlines a
framework built on a multilayer perceptron neural network model
capable of achieving this goal
Original languageEnglish
Title of host publication IEEE International Conference on Cyber Situational Awareness, Data Analytics and Assessment (Cyber SA 2018)
Pages12
Number of pages20
DOIs
Publication statusPublished - 6 Jun 2018
Event IEEE International Conference on Cyber Situational Awareness, Data Analytics and Assessment (Cyber SA 2018) - Scotland, Glasgow, United Kingdom
Duration: 11 Jun 201812 Jun 2018

Conference

Conference IEEE International Conference on Cyber Situational Awareness, Data Analytics and Assessment (Cyber SA 2018)
CountryUnited Kingdom
CityGlasgow
Period11/06/1812/06/18

Keywords

  • vpn
  • Neural Network
  • Encryption

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  • Cite this

    Miller, S., Curran, K., & Lunney, T. (2018). Multilayer Perceptron Neural Network for Detection of Encrypted VPN Network Traffic. In IEEE International Conference on Cyber Situational Awareness, Data Analytics and Assessment (Cyber SA 2018) (pp. 12) https://doi.org/10.1109/CyberSA.2018.8551395