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
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 language | English |
|---|---|
| Title of host publication | IEEE International Conference on Cyber Situational Awareness, Data Analytics and Assessment (Cyber SA 2018) |
| Pages | 12 |
| Number of pages | 20 |
| DOIs | |
| Publication status | Published (in print/issue) - 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 2018 → 12 Jun 2018 |
Conference
| Conference | IEEE International Conference on Cyber Situational Awareness, Data Analytics and Assessment (Cyber SA 2018) |
|---|---|
| Country/Territory | United Kingdom |
| City | Glasgow |
| Period | 11/06/18 → 12/06/18 |
Keywords
- vpn
- Neural Network
- Encryption