Jalal Atoum

Work place: Princess Sumaya University for Technology (PSUT), Amman, Jordan

E-mail: atoum@psut.edu.jo


Research Interests: Computer Science & Information Technology, Information Systems, Multimedia Information System, Social Information Systems


Prof. Atoum is currently the Dean of The King Hussein School of Computing Sciences at Princess Sumaya University for Technology (PSUT). He had received his B.S. degree in Computer Science from Yarmouk university-Jordan in 1984. He had received his Master degree in Computer Science from University of Texas at Arlington-USA in 1987. He had received his PhD in Computer Science from University of Houston-USA in 1993.  He had worked as an assistant professor at Yarmouk University from 1993 to 1995.  He had been appointed as the Computer Science department Chairman at PSUT. He has supervised or co-supervised several students on their Ph.D. dissertations and several M.S. theses and has supervised numerous undergraduate graduation projects. Finally, he have been involved in several committees for degree plans, proposed and developed the Master program in Information System Security and Digital Criminology at PSUT.

Author Articles
A Model for Detecting Tor Encrypted Traffic using Supervised Machine Learning

By Alaeddin Almubayed Ali Hadi Jalal Atoum

DOI: https://doi.org/10.5815/ijcnis.2015.07.02, Pub. Date: 8 Jun. 2015

Tor is the low-latency anonymity tool and one of the prevalent used open source anonymity tools for anonymizing TCP traffic on the Internet used by around 500,000 people every day. Tor protects user’s privacy against surveillance and censorship by making it extremely difficult for an observer to correlate visited websites in the Internet with the real physical-world identity. 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 defences like TLS encryption, padding, and packet relaying. However, in this paper, an analysis has been performed against Tor from a local observer in order to bypass Tor protections; the method consists of a feature extraction from a local network dataset. Analysis shows that it’s still possible for a local observer to fingerprint top monitored sites on Alexa and Tor traffic can be classified amongst other HTTPS traffic in the network despite the use of Tor’s protections. In the experiment, several supervised machine-learning algorithms have been employed. The attack assumes a local observer sitting on a local network fingerprinting top 100 sites on Alexa; results gave an improvement amongst previous results by achieving an accuracy of 99.64% and 0.01% false positive.

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