Abstract
Network Proxies and Virtual Private Networks (VPN) are tools that are used every day to facilitate various business functions. However, they have gained popularity amongst unintended userbases as tools that can be used to hide mask identities while using websites and web-services. Anonymising Proxies and/or VPNs act as an intermediary between a user and a web server with a Proxy and/or VPN IP address taking the place of the user’s IP address that is forwarded to the web server. This paper presents computational models based on intelligent machine learning techniques to address the limitations currently experienced by unauthorised user detection systems. A model to detect usage of anonymising proxies was developed using a Multi-layered perceptron neural network that was trained using data found in the Transmission Control Protocol (TCP) header of captured network packets
Highlights
The Internet has become an important part of everyday life and its usage continues to grow as more devices are released that have Internet connectivity
Anonymising proxies and/or virtual private networks (VPN) act as an intermediary between a user and a web server with a proxy and/or VPN IP address taking the place of the user’s IP address that is forwarded to the web server
While proxies and VPNs have legitimate uses, such as connecting to a business network from a remote location, they are still abused by criminals who use them to commit crimes whilst remaining undetected and unidentified (Poh & Divakaran, 2021)
Summary
The Internet has become an important part of everyday life and its usage continues to grow as more devices are released that have Internet connectivity. As more people use the Internet, governments seek to implement controls on what their citizens can access, either for the protection of said citizens against malware and identity theft or to suppress unacceptable parts of the Internet (Akabogu, 2016; Fiaschi et al, 2017). This leads some people to become concerned for their privacy as they do not want their online activities documented. Other techniques include monitoring architectures for the cloud and secure agent computing (Munoz et al, 2013; Maña et al, 2007)
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