Therefore, this paper suggests two machine learning models that categorize network traffic into encrypted and non-encrypted traffic. In this paper, the classification of network traffic data in terms of VPN and non-VPN traffic is studied based on the efficiency of time-based features extracted from network packets. Therefore, an efficient approach is specially needed that enables the identification of encrypted network traffic data to extract and select valuable features which improve the quality of service and network management as well as to oversee the overall performance. The method of categorizing encrypted traffic is one of the most challenging issues introduced by a VPN as a way to bypass censorship as well as gain access to geo-locked services. This affects and complicates the quality of service (QoS), traffic monitoring, and network security provided by Internet Service Providers (ISPs), particularly for analysis and anomaly detection approaches based on the network traffic’s nature. Furthermore, with the increase in the adoption of encrypted data transmission by many people who tend to use a Virtual Private Network (VPN) or Tor Browser (dark web) to keep their data privacy and hidden, network traffic encryption is rapidly becoming a universal approach. The COVID-19 pandemic has resulted in a massive volume of data flow on the Internet, as many employees have transitioned to working from home. The continual growth of the use of technological appliances during One can find experiment details in the later chapters. Eventually, our work concludes with the experiment of the de-anonymization process on the internet traffic simulation framework created by container technology. We also used the docker-compose plugin to provision Docker containers.
#GRAPHIC VPN MONITOR SOFTWARE#
In this research, we used vagrant to provision our virtual machines and docker-compose to use lightweight versions of the mentioned software and tools above. Later, with the background research, we will provide design details of our traffic generation framework to create realistic internet traffic. We also gave shallow information about those terms. Our work starts by anonymizing the connection in the work environment with VPN(Virtual Private Network) by using OpenVPN, TOR(the onion routing project), or Wireguard. We will explain what means such security terms, e.g., anonymization and (de)anonymization. We will give shallow information about why we chose Docker to build our test environment.
During this research, we will choose container technology, e.g.docker and achieve isolation using container technology instead of the traditional virtual machine(VM) approach. This research will simulate an internet traffic environment and eventually make a (de)anonymization lab.
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