• Raval Radha
• Deepak Upadhyay

Tor is an anonymous Internet communication system based on the second generation of onion routing network protocol. Tor protects user’s freedom privacy against surveillance and censorship by making it more difficult for an observer to correlate visited websites in the Internet with the real physical-world identity. Sometimes this browser can used by cyber criminal to do their illegal activities. Tor accomplished that by ensuring adequate protection of Tor traffic against traffic analysis and feature extraction techniques. Further, Tor ensures anti-website fingerprinting by implementing different defenses like TLS encryption, padding, and packet relaying. In this paper proposed technique able to identify whether host is generating Tor-related traffic. For that well-known deep learning algorithms in order to evaluate the effectiveness of the proposed feature set in a real world environment. In addition this technique demonstrates that the proposed method is able to recognize the kind of activity the user under analysis is doing on the Tor network.

FFN, DL, HTTP, SVM, VOIP, P2P, FTP, HMM

"Tor Traffic Classifiaction using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.360-366, May-2019, Available :http://www.jetir.org/papers/JETIR1905555.pdf

"Tor Traffic Classifiaction using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp360-366, May-2019, Available at : http://www.jetir.org/papers/JETIR1905555.pdf