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General Information
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2014 Vol.6(1): 57-62 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2014.V6.837

Improving Operating System Fingerprinting using Machine Learning Techniques

Taher Al-Shehari and Farrukh Shahzad
Abstract—Operating System (OS) detection is one of the main concerns for computer security. The previous works that have been done on operating system detection, exploit some features of TCP/IP traffic based on a single packet. In this work, we built a system where TCP/IP communication is setup between machines to capture and analyze TCP/IP packets for more accurate and fine grained OS detection using our novel packet correlation approach. We used existing signature matching methods, extend it and employed machine learning techniques to detect remote operating systems with improved accuracy. We also employed mobile systems like smart phones and tablets to perform mobile OS fingerprinting. The tools we created also established encrypted communication using Secure Socket Layer (SSL) network protocol to investigate the effect of SSL communication on OS fingerprinting. The result of our experimental work showed that fine grained OS detection can be achieved for modern and mobile OSs using our approach.

Index Terms—OS fingerprinting, remote operating system detection, vulnerability assessment, mobile operating system.

The authors are with ICS department at King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia (e-mail: g200905290@kfupm.edu.sa, farrukhshahzad@kfupm.edu.sa).


Cite:Taher Al-Shehari and Farrukh Shahzad, "Improving Operating System Fingerprinting using Machine Learning Techniques," International Journal of Computer Theory and Engineering vol. 6, no. 1, pp. 57-62, 2014.

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