Abstract: Tor (The Onion Router) is an anonymity tool that is widely used worldwide. Tor protect its user privacy against surveillance and censorship using strong encryption and obfuscation techniques which makes it extremely difficult to monitor and identify users’ activity on the Tor network. It also implements strong defense to protect the users against traffic features extraction and website fingerprinting. However, the strong anonymity also became the heaven for criminal to avoid network tracing. Therefore, numerous of research has been performed on encrypted traffic analyzing and classification using machine learning techniques. This paper presents survey on existing approaches for classification of Tor and other encrypted traffic. There is preliminary discussion on machine learning approaches and Tor network. Next, there are comparison of the surveyed traffic classification and discussion on their classification properties.

Mohamad Amar Irsyad Mohd Aminuddin, Zarul Fitri Zaaba, Manmeet Kaur Mahinderjit Singh and Darshan Singh Mahinder Singh, “A Survey on Tor Encrypted Traffic Monitoring” International Journal of Advanced Computer Science and Applications(IJACSA), 9(8), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090815

@article{Aminuddin2018,
title = {A Survey on Tor Encrypted Traffic Monitoring},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090815},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090815},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {8},
author = {Mohamad Amar Irsyad Mohd Aminuddin and Zarul Fitri Zaaba and Manmeet Kaur Mahinderjit Singh and Darshan Singh Mahinder Singh}
}