Virtual Private Network Flow Detection in Wireless Sensor Networks Using Machine Learning Techniques

Title:Virtual Private Network Flow Detection in Wireless Sensor Networks Using Machine Learning Techniques

Volume: 11 Issue: 7

Author(s): Surapaneni Phani Praveen*, Thati Bala Murali Krishna, Sunil Kumar Chawla and Chokka Anuradha

Affiliation:

• Department of Computer Science and Engineering (CSE), Prasad V Potluri Siddhartha Institute of Technology, Vijayawada,India

Keywords: WSN, HTTPS, DNS, VPN, classification, feature extraction, traffic classification.

Abstract:

Background: Every organization generally uses a VPN service individually to bypass the filters that hide the actual communication. Such communication filtration is not allowed by the organizational monitoring network. But these institutes are not in a position to spend a considerable amount of funds on a secure sockets layer to monitor traffic flow over their computer networks.

Objective: Our work suggests a simple technique to block or detect annoying VPN clients inside the network activities. This method does not require the network to decrypt or even decode any network communication.

Methods: The proposed solution selects two machine learning techniques Feature Tree and K-means as classification techniques that work on time-related features. First, the DNS mapping with the ordinary characteristic of the transmission control protocol / Internet protocol computer the network stack is identified, and it is not to be considered as a regular traffic flow if the domain name information is not available. The process not only examines non-standard utilization of hypertext transfer protocol security but also conceals such communication from hypertext transfer protocol security dependent filters in the firewall to detect as an anomaly in large.

Results: We define the traffic flow as normal traffic flow and VPN traffic flow. These two flows are characterized by taking two machine learning techniques, Feature Tree and K-means. We executed each experiment 4 times. As a result, eight types of regular traffics and eight types of VPN traffics were represented.

Conclusion: Once the traffic flow is identified, it is classified and studied by machine learning techniques. Using time-related features, the traffic flow is defined as normal flow or VPN traffic flow.

Cite this article as:

Praveen Phani Surapaneni *, Krishna Murali Thati Bala, Chawla Kumar Sunil and Anuradha Chokka, Virtual Private Network Flow Detection in Wireless Sensor Networks Using Machine Learning Techniques, International Journal of Sensors, Wireless Communications and Control 2021; 11 (7) . https://dx.doi.org/10.2174/2210327910666210104160027