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Reference
➢ G. Baldini and G. Steri, “Survey of techniques for the identification of mobile using the physical fingerprints of the
built-in components,” IEEE Communications Surveys & Tutorials, vol. 19, no. 3, pp. 1761–1789, 2017.
➢ B. D. Fulcher and N. S. Jones, “Highly comparative feature-based time-series classification,” IEEE Transactions on
Knowledge and Data Engineering, vol. 26, no. 12, pp. 3026–3037, 2014.
➢ J. E. V. Ferreira, C. H. S. da Costa, R. M. de Miranda, and A. F. de Figueiredo, “The use of the k nearest neighbor method
to classify the representative elements,” Educación Química, vol. 26, no. 3, pp. 195 – 201, 2015. [Online]. Available:
http://www.sciencedirect.com/science/article/pii/S0187893X15000312
➢ P. Mulak and N. Talhar, “Analysis of distance measures using k-nearest neighbor algorithm on kdd dataset,”
International Journal of Science and Research, vol. 4, no. 7, pp. 2101–2104, 2015.
➢ T. Kohno, A. Broido, and K. C. Claffy, “Remote physical device fingerprinting,” IEEE Transactions on Dependable and
Secure Computing, vol. 2, no. 2, pp. 93–108, 2005.
➢ M. Cristea and B. Groza, “Fingerprinting smartphones remotely via ICMP timestamps,” IEEE Communications Letters,
vol. 17, no. 6, pp. 1081–1083, 2013.](https://image.slidesharecdn.com/presentation-eleko2-181202154016/85/Device-Fingerprinting-for-Authentication-12-320.jpg)
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Device Fingerprinting for Authentication
The document discusses device fingerprinting as a method for identifying and authenticating devices using internal and external characteristics, particularly through transmission control protocol in mobile devices. It details a methodology involving network device spectrum analysis and data classification, highlighting promising results for identifying mobile devices, especially between the iPhone 7 and Xiaomi Mi A1. Future work aims for more advanced classification techniques and the necessity for testing a broader range of devices to enhance security.
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Device Fingerprinting for Authentication
- 1. 2018 ELECO Zulfidin Khodzhaev1 , CemAyyıldız2 , Güneş Karabulut Kurt1 Istanbul Technical University1 , Giga OHM2 Device Fingerprinting for Authentication
- 2. ➢ What isdevice fingerprinting ? It is a technique that is used to identify and authenticate a device ➢ Methods used in device fingerprinting ? Internal - imperfections of built-in components of the device External - radio frequency (RF) emissions of the device ➢ Preferred method in our research ? Transmission control protocol - reliable in precision which is intrinsic for device functionality has unique characteristics for every device Introduction 1
- 3. Research Question How to identify adevice ? 2 Samsung
- 4. Why is it Important? 3 Tracking Security
- 5. ➢ Using internalcomponents exploit the Internet Control Message ➢ Protocol (ICMP) timestamp-based fingerprinting to identify mobile phones over a WLAN 4 ❏ M. Cristea and B. Groza, “Fingerprinting smartphones remotely via ICMP timestamps,” IEEE Communications Letters, vol. 17, no. 6, pp. 1081–1083, 2013. Related Research
- 6. ➢ Device manipulationto send and receive data over TCP ➢ Identification of channel of the Wi-Fi network and tuning the center of frequency ➢ Averaging samples and reducing peak to peak noise ➢ Visualizing obtained data and finding relative classification techniques ➢ Classification of signals results in identification and authentication of a device Methodology Network Device Spectrum Analyzer Data Classifier Device2Device1 5
- 7. ➢ Calculation ofdistance between neighboring instances ➢ The training data - to make a prediction ➢ Testing data - to assess predictions made ➢ Calculation of distance between trained data and testing data ➢ Data is classified into a group in which the distance is the shortest Technique 6 Data Instance1 Training Instance2 Instance3 Other Instances Testing Distance Device1 Device2
- 8.
- 9. Test setup Jetson TX2 iPhone7 Xiaomi Mi A1 NI PXIe-1082 8
- 10. Result and Discussion Iphone 7Xiaomi Mi A1 7 9 K=3 100% 56% K=4 100% 56% K=5 100% 64% ➢ Importance of nearest instances
- 11. ➢ Results formobile devices seem to be promising ➢ More advanced classification techniques needed ➢ Different devices should be tested ➢ Electronic devices are part of our society and they should be as secure as possible ➢ DSL and ADSL modems 10 Conclusion and future research
- 12. 11 Reference ➢ G. Baldiniand G. Steri, “Survey of techniques for the identification of mobile using the physical fingerprints of the built-in components,” IEEE Communications Surveys & Tutorials, vol. 19, no. 3, pp. 1761–1789, 2017. ➢ B. D. Fulcher and N. S. Jones, “Highly comparative feature-based time-series classification,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 12, pp. 3026–3037, 2014. ➢ J. E. V. Ferreira, C. H. S. da Costa, R. M. de Miranda, and A. F. de Figueiredo, “The use of the k nearest neighbor method to classify the representative elements,” Educación Química, vol. 26, no. 3, pp. 195 – 201, 2015. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0187893X15000312 ➢ P. Mulak and N. Talhar, “Analysis of distance measures using k-nearest neighbor algorithm on kdd dataset,” International Journal of Science and Research, vol. 4, no. 7, pp. 2101–2104, 2015. ➢ T. Kohno, A. Broido, and K. C. Claffy, “Remote physical device fingerprinting,” IEEE Transactions on Dependable and Secure Computing, vol. 2, no. 2, pp. 93–108, 2005. ➢ M. Cristea and B. Groza, “Fingerprinting smartphones remotely via ICMP timestamps,” IEEE Communications Letters, vol. 17, no. 6, pp. 1081–1083, 2013.
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