Abstract
The security of cryptographic systems is a major concern for cryptosystem designers, even though cryptography algorithms have been improved. Side-channel attacks, by taking advantage of physical vulnerabilities of cryptosystems, aim to gain secret information. Several approaches have been proposed to analyze side-channel information, among which machine learning is known as a promising method. Machine learning in terms of neural networks learns the signature (power consumption and electromagnetic emission) of an instruction, and then recognizes it automatically. In this paper, a novel experimental investigation was conducted on field-programmable gate array (FPGA) implementation of elliptic curve cryptography (ECC), to explore the efficiency of side-channel information characterization based on a learning vector quantization (LVQ) neural network. The main characteristics of LVQ as a multi-class classifier are that it has the ability to learn complex non-linear input-output relationships, use sequential training procedures, and adapt to the data. Experimental results show the performance of multi-class classification based on LVQ as a powerful and promising approach of side-channel data characterization.
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References
Bartkewitz, T., Lemke-Rust, K., 2013. Efficient template attacks based on probabilistic multi-class support vector machines. LNCS, 7771: 263–276. http://dx.doi.org/10.1007/978-3-642-37288-9_18
Blake, I.F., Seroussi, G., Smart, N., 1999. Elliptic Curves in Cryptography. Cambridge University Press. http://dx.doi.org/10.1017/CBO9781107360211
Cybenko, G., 1989. Approximation by superpositions of a sigmoidal function. Math. Contr. Signals Syst., 2(4): 303–314. http://dx.doi.org/10.1007/BF02551274
de Mulder, E., Buysschaert, P., Ors, S.B., et al., 2005. Electromagnetic analysis attack on an FPGA implementation of an elliptic curve cryptosystem. Int. Conf. on Computer as a Tool, p.1879–1882. http://dx.doi.org/10.1109/EURCON.2005.1630348
Duda, R.O., Hart, P.E., Stork, D.G., 2011. Pattern Classification. John Wiley & Sons.
Flotzinger, D., Kalcher, J., Pfurtscheller, G., 1992. EEG classification by learning vector quantization. Biomed. Eng., 37(12): 303–309 (in German). http://dx.doi.org/10.1515/bmte.1992.37.12.303
Gersho, A., 1979. Asymptotically optimal block quantization. IEEE Trans. Inform. Theory, 25(4): 373–380. http://dx.doi.org/10.1109/TIT.1979.1056067
Haykin, S.S., 2009. Neural Networks and Learning Machines. Pearson Education, Upper Saddle River.
Heuser, A., Zohner, M., 2012. Intelligent machine homicide. Int. Workshop on Constructive Side-Channel Analysis and Secure Design, p.249–264. http://dx.doi.org/10.1007/978-3-642-29912-4_18
Heyszl, J., Mangard, S., Heinz, B., et al., 2012a. Localized electromagnetic analysis of cryptographic implementations. Cryptographers’ Track at the RSA Conf., p.231–244. http://dx.doi.org/10.1007/978-3-642-27954-6_15
Heyszl, J., Merli, D., Heinz, B., et al., 2012b. Strengths and limitations of high-resolution electromagnetic field measurements for side-channel analysis. Int. Conf. on Smart Card Research and Advanced Applications, p.248–262. http://dx.doi.org/10.1007/978-3-642-37288-9_17
Itoh, K., Izu, T., Takenaka, M., 2002. Address-bit differential power analysis of cryptographic schemes OK-ECDH and OK-ECDSA. LNCS, 2523: 129–143. http://dx.doi.org/10.1007/3-540-36400-5_11
Koblitz, N., 1987. Elliptic curve cryptosystems. Math. Comput., 48(177): 203–209. http://dx.doi.org/10.1090/S0025-5718-1987-0866109-5
Kocher, P., Jaffe, J., Jun, B., 1999. Differential power analysis. Annual Int. Cryptology Conf., p.388–397. http://dx.doi.org/10.1007/3-540-48405-1_25
Kohonen, T., 1988. An introduction to neural computing. Neur. Networks, 1(1): 3–16. http://dx.doi.org/10.1016/0893-6080(88)90020-2
Kohonen, T., 1990a. Improved versions of learning vector quantization. Int. Joint Conf. on Neural Networks, p.545–550. http://dx.doi.org/10.1109/IJCNN.1990.137622
Kohonen, T., 1990b. Statistical pattern recognition revisited. In: Eckmiller, R. (Ed.), Advanced Neural Computers. North-Holland, Amsterdam, p.137–144. http://dx.doi.org/10.1016/B978-0-444-88400-8.50020-0
Kopf, B., Durmuth, M., 2009. A provably secure and efficient countermeasure against timing attacks. 22nd IEEE Computer Security Foundations Symp., p.324–335. http://dx.doi.org/10.1109/CSF.2009.21
Li, C., Lee, C., 2011. A robust remote user authentication scheme using smart card. Inform. Technol. Contr., 40(3): 236–245. http://dx.doi.org/10.5755/j01.itc.40.3.632
Ma, C., Wang, D., Zhang, Q., 2012. Cryptanalysis and improvement of Sood et al.’s dynamic ID-based authentication scheme. Int. Conf. on Distributed Computing and Internet Technology, p.141–152. http://dx.doi.org/10.1007/978-3-642-28073-3_13
Ma, C., Wang, D., Zhao, S., 2014. Security flaws in two improved remote user authentication schemes using smart cards. Int. J. Commun. Syst., 27(10): 2215–2227. http://dx.doi.org/10.1002/dac.2468
Mangard, S., Oswald, E., Popp, T., 2007. Power Analysis Attacks: Revealing the Secrets of Smart Cards. Springer Science & Business Media. http://dx.doi.org/10.1007/978-0-387-38162-6
Mäntysalo, J., Torkkolay, K., Kohonen, T., 1992. LVQbased speech recognition with high-dimensional context vectors. Int. Conf. on Spoken Language Processing, p.539–542.
Miller, V.S., 1986. Use of elliptic curves in cryptography. Conf. on the Theory and Application of Cryptographic Techniques, p.417–426. http://dx.doi.org/10.1007/3-540-39799-X_31
Msgna, M., Markantonakis, K., Mayes, K., 2014. Precise instruction-level side channel profiling of embedded processors. Int. Conf. on Information Security Practice and Experience, p.129–143. http://dx.doi.org/10.1007/978-3-319-06320-1_11
Orlando, J., Mann, R., Haykin, S., 1990. Radar Classification of Sea-Ice Using Traditional and Neural Classifiers. Proc. Int. Joint Conf. on Neural Networks, II-263.
Pregenzer, M., Pfurtscheller, G., Flotzinger, D., 1996. Automated feature selection with a distinction sensitive learning vector quantizer. Neurocomputing, 11(1): 19–29. http://dx.doi.org/10.1016/0925-2312(94)00071-9
Prouff, E., 2014. Constructive Side-Channel Analysis and Secure Design. Springer Berlin Heidelberg. http://dx.doi.org/10.1007/978-3-319-10175-0
Saeedi, E., Kong, Y., 2014. Side channel information analysis based on machine learning. 8th Int. Conf. on Signal Processing and Communication Systems, p.1–7. http://dx.doi.org/10.1109/ICSPCS.2014.7021075
Saeedi, E., Hossain, M.S., Kong, Y., 2015. Multi-class SVMs analysis of side-channel information of elliptic curve cryptosystem. Int. Symp. on Performance Evaluation of Computer and Telecommunication Systems, p.1–6. http://dx.doi.org/10.1109/SPECTS.2015.7285297
Tillich, S., Herbst, C., 2008. Attacking state-of-the-art software countermeasures: a case study for AES. Int. Workshop on Cryptographic Hardware and Embedded Systems, p.228–243. http://dx.doi.org/10.1007/978-3-540-85053-3_15
Wang, D., Wang, P., 2015. Offline dictionary attack on password authentication schemes using smart cards. LNCS, 7807: 221–237. http://dx.doi.org/10.1007/978-3-319-27659-5_16
Wang, D., Ma, C., Zhang, Q., et al., 2013. Secure passwordbased remote user authentication scheme against smart card security breach. J. Networks, 8(1): 148–155.
Wang, D., He, D., Wang, P., et al., 2015a. Anonymous twofactor authentication in distributed systems: certain goals are beyond attainment. IEEE Trans. Depend. Sec. Comput., 12(4): 428–442. http://dx.doi.org/10.1109/TDSC.2014.2355850
Wang, D., Wang, N., Wang, P., et al., 2015b. Preserving privacy for free: efficient and provably secure two-factor authentication scheme with user anonymity. Inform. Sci., 321: 162–178. http://dx.doi.org/10.1016/j.ins.2015.03.070
Yeh, K., 2015. A lightweight authentication scheme with user untraceability. Front. Inform. Technol. Electron. Eng., 16(4): 259–271. http://dx.doi.org/10.1631/FITEE.1400232
Zador, P.L., 1982. Asymptotic quantization error of continuous signals and the quantization dimension. IEEE Trans. Inform. Theory, 28(2): 139–149. http://dx.doi.org/10.1109/TIT.1982.1056490
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ORCID: Ehsan SAEEDI, http://orcid.org/0000-0002-0879-113X
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Saeedi, E., Kong, Y. & Hossain, M.S. Side-channel attacks and learning-vector quantization. Frontiers Inf Technol Electronic Eng 18, 511–518 (2017). https://doi.org/10.1631/FITEE.1500460
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DOI: https://doi.org/10.1631/FITEE.1500460
Key words
- Side-channel attacks
- Elliptic curve cryptography
- Multi-class classification
- Learning vector quantization