2009 IEEE Conference on Emerging Technologies &Amp; Factory Automation 2009

DOI: 10.1109/etfa.2009.5347095

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A new method for recognizing operating systems of automation devices

Jorge L. Medeiros

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,

Agostinho M. Brito

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,

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Cited by 7 publications

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“…Active probing of targeted system is adopted by [2,5,19,27,30,33,41,43]. Passive and hybrid schemes are studied as well [18,26].…”

Section: Related Workmentioning

confidence: 99%

OS Fingerprinting and Tethering Detection in Mobile Networks

¹

,

Liao²,

et al. 2014

Proceedings of the 2014 Conference on Internet Measurement Conference

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Fingerprinting the Operating System (OS) running on a device based on its traffic has several applications, such as NAT detection, policy enforcement in enterprise networks, and billing for shared access in mobile networks. In this paper, we propose to utilize several features in TCP/IP headers for OS identification, and use real traffic traces to evaluate the accuracy of fingerprinting. Our tracedriven study shows that several techniques that successfully fingerprint desktop OSes are not effective for fingerprinting mobile devices. Therefore, we propose new features for fingerprinting OSes on mobile devices. We also consider NAT/tethering detection, an important application of OS fingerprinting. We use the presence of multiple OSes from the same IP address along with TCP timestamp, clock frequency, and boot time to detect tethering. Evaluation shows that our approach effectively detects tethering and outperforms existing schemes.

“…Active probing of targeted system is adopted by [2,5,19,27,30,33,41,43]. Passive and hybrid schemes are studied as well [18,26].…”

Section: Related Workmentioning

confidence: 99%

OS Fingerprinting and Tethering Detection in Mobile Networks

¹

,

Liao²,

et al. 2014

Proceedings of the 2014 Conference on Internet Measurement Conference

View full text Add to dashboard Cite

Fingerprinting the Operating System (OS) running on a device based on its traffic has several applications, such as NAT detection, policy enforcement in enterprise networks, and billing for shared access in mobile networks. In this paper, we propose to utilize several features in TCP/IP headers for OS identification, and use real traffic traces to evaluate the accuracy of fingerprinting. Our tracedriven study shows that several techniques that successfully fingerprint desktop OSes are not effective for fingerprinting mobile devices. Therefore, we propose new features for fingerprinting OSes on mobile devices. We also consider NAT/tethering detection, an important application of OS fingerprinting. We use the presence of multiple OSes from the same IP address along with TCP timestamp, clock frequency, and boot time to detect tethering. Evaluation shows that our approach effectively detects tethering and outperforms existing schemes.

Learning Remote Computer Fingerprinting

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et al. 2014

Studies in Computational Intelligence

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A Qualitative Survey of Active TCP/IP Fingerprinting Tools and Techniques for Operating Systems Identification

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²

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2011

Computational Intelligence in Security for Information Systems

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