Abstract
The article is devoted to dynamic authentication method of electronic network devices with built-in analog-to-digital converters (ADCs) based on authentication templates. The following results were obtained: the authentication of each electronic device can be carried out uniquely by its internal electrical noise (like biometric authentication of a person). Uniqueness of authentication is provided by the invariants of the noise signal such as the shape of the graph of the autocorrelation function of noise and the set of resonance frequencies of the device. The electronic device authentication template is obtained from the sequence of values of the autocorrelation function of the noise. It consists from the bit template and the amplitude template. The technique of obtaining an authentication template is presented. The required duration of the noise signal is 0.5 s for reliable authentication at a sampling frequency of 44.1 kHz. The results of authentication of several computers are presented.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hasse, J., Gloe, T., Beck, M.: Forensic Identification of GSM Mobile Phones. http://www.dence.de/publications/Hasse13_GSMMobilePhoneIdentification.pdf. Accessed 19 Mar 2018
Toshiba Develops New Chip Authentication Technology Using Transistor Noise. http://www.toshiba.co.jp/rdc/rd/detail_e/e1506_03.html. Accessed 19 Mar 2018
Laput, G., Yang, C., Xiao, R., Sample, A., Harrison, C.: Em-sense: touch recognition of uninstrumented, electrical and electromechanical objects. In: 28th Annual ACM Symposium on User Interface Software Technology, pp. 157–166. UIST, New York (2015)
Karapanos, N., Marforio, C., Soriente, C., Capkun, S.: Sound-proof: usable two-factor authentication based on ambient sound. In: 24th USENIX Security Symposium, pp. 483–498. USENIX, Washington (2015)
Yang, C., Sample, A.P.: EM-ID: tag-less identification of electrical devices via electromagnetic emissions (2016). http://ieeexplore.ieee.org/document/7488014/
Chumachenko, A., Rublev, D., Makarevich, O., Fedorov, V.: Identification of digital microphones by the imperfections of the recording path. Issue SFU Tech. Sci. Thematic Issue Inf. Secur. Taganrog 8, 84–92 (2007)
Rybalsky, O., Zhuravel, V., Solovyev, V.: Signalogramm structure and universality of the fractal approach to the development of the phonoscope assessment toolkit. Inf. Math. Methods Simul. 3(3), 225–232 (2013)
Nyemkova, E., Chaplyha, V., Shandra, Z.: Technique of measuring of identification parameters of audio recording device. In: The 18th International Conference on Information Technology for Practice, Ostrava, pp. 209–218 (2015)
Nyemkova, E., Chaplyha, V., Shandra, Z., Kochan, R., Gancarczyk, T., Shaikhanova, A.: Computational device authentication via fluctuations of analog-to-digital converter. In: 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (2017)
Nicolis, G., Prigogine, I.: Self-Organization in Nonequilibrium Systems. Wiley-Interscience, New York (1977)
Ebeling, W.: Stochastische Theorie der Nichtlinearen Irreversiblen Prozesse. W. Pieck U.P, Rostock (1977)
Mehrotra, A.: Simulation and modelling techniques for noise in radio frequency integrated circuits. University of California at Berkeley (1999)
Schuster, H.G., Just, W.: Deterministic Chaos. WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim (2005)
Rabinovich, M.I., Afraimovich, V.S.: Stochastic auto oscillations and turbulence. Phys. Usp. 125, 123–168 (1978)
Lichtenberg, A., Lieberman, M.: Regular and Chaotic Dynamics. Springer, New York (1992). https://doi.org/10.1007/978-1-4757-2184-3
Loskutov, A.: Lectures time series analysis. http://chaos.phys.msu.ru/loskutov/PDF/Lectures_time_series_analysis.pdf. Accessed 19 Mar 2018
Kuzovlev, Y.: Why nature needs 1/f noise. Phys. Usp. 58(7), 719–729 (2015)
Dyvak M., Padletska N., Pukas, A., Kozak O.: Identification the recurrent laryngeal nerve by the autocorrelation function of signal as reaction on the stimulation of tissues in surgical wound. In: Proceedings of the XIIth International Conference CADSM 2013, Lviv, Ukraine, pp. 89–92 (2013)
Nikulchev, E.B.: Identification of Dynamic Systems Based on Symmetry of Reconstructed Attractors. Moscow State University of Printing Publishing, Moscow (2010)
Petrovich, V.N.: Identification of parameters of mathematical models of dynamic control system. Artif. Intell. 4, 343–349 (2011)
Patra, J.C.: Identification of nonlinear dynamic systems using functional link artificial neural networks. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 29(2), 254–262 (1999)
Patra, J.C., Kot, A.C.: Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 32(4), 505–511 (2002)
OscilloMeter 7.30 - Multichannel Real-Time Spectrum Analyzer. http://soft-arhiv.com/load/47-1-0-95
Max, J.: Methods and Techniques for Signal Processing and Applications to Physical Measurements - Principles and Apparatus for Real-Time Processing. Masson, Paris (1980)
Bendat, J.S., Piersol, A.G.: Random Data: Analysis and Measurement Procedures. Wiley, New York (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Nyemkova, E., Shandra, Z., Kłos-Witkowska, A., Więcław, Ł. (2018). Network Electronic Devices Authentication by Internal Electrical Noise. In: Saeed, K., Homenda, W. (eds) Computer Information Systems and Industrial Management. CISIM 2018. Lecture Notes in Computer Science(), vol 11127. Springer, Cham. https://doi.org/10.1007/978-3-319-99954-8_39
Download citation
DOI: https://doi.org/10.1007/978-3-319-99954-8_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99953-1
Online ISBN: 978-3-319-99954-8
eBook Packages: Computer ScienceComputer Science (R0)