Pars Anonymity Network Traffic Flow Analysis using Machine Learning

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Homayun H.; DEHGHANI M.; AKBARI H.

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Journal Paper

Paper Information

Journal: Scientific Journal of Passive Defense Year:2021 | Volume:12 | Issue:2 (46) Page(s): 1-17

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Information Journal Paper

Title

Pars Anonymity Network Traffic Flow Analysis using Machine Learning

Author(s)

Homayun H. | DEHGHANI M. | AKBARI H. | Issue Writer Certificate

Keywords

Anonymity

Anonymity Network

Data Mining

Classification

Machine Learning

Traffic Analysis

Abstract

One of the common network security and Anonymity methods, is the use of Anonymity networks. Pars Anonymity Network is a domestic anonymizer network, developed by Iranian specialists. One of the main weaknesses of anonymous networks is their traffic differentiation and recognition among other network traffic. Uncovering the traffic passing through a network, means recognizing the nature of that traffic, and if this traffic is the traffic of an Anonymity tool, it means that confidential information is being exchanged in the network, which puts Anonymity in danger. One of the evaluation criteria of Anonymity networks, is undifferentiability and indistinguishability of anonymous network traffic from normal traffic. Traffic Classification-which has various applications-is one of the most powerful methods in Data Mining. Traffic management via detecting network traffic flow, is considered as one of these applications. In this research, by using Data Mining techniques, in the first step the detection rate of Pars Anonymity Network is assessed in comparison to the Onion Router, Invisible Internet Project, JonDo and HTTPS traffics, and in the next step, the Classification rate of four different services in the desired anonymizer is studied in more detail. Results suggest that the Classification accuracy rate of these experiments in the first step is 100% and in the next step-with the use of Random Forest algorithm-is 95%. Furthermore, by evaluating the used specifications in every experiment, the effectiveness of these specifications regarding the overall accuracy and the model construction time is assessed.

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APA: Copy

Homayun, H., DEHGHANI, M., & AKBARI, H.. (2021). Pars Anonymity Network Traffic Flow Analysis using Machine Learning. JOURNAL OF PASSIVE DEFENSE, 12(2 (46) ), 1-17. SID. https://sid.ir/paper/986080/en

Vancouver: Copy

Homayun H., DEHGHANI M., AKBARI H.. Pars Anonymity Network Traffic Flow Analysis using Machine Learning. JOURNAL OF PASSIVE DEFENSE[Internet]. 2021;12(2 (46) ):1-17. Available from: https://sid.ir/paper/986080/en

IEEE: Copy

H. Homayun, M. DEHGHANI, and H. AKBARI, “Pars Anonymity Network Traffic Flow Analysis using Machine Learning,” JOURNAL OF PASSIVE DEFENSE, vol. 12, no. 2 (46) , pp. 1–17, 2021, [Online]. Available: https://sid.ir/paper/986080/en

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