Side-channel attacks and learning-vector quantization

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.

Keywords

Side-channel attacks / Elliptic curve cryptography / Multi-class classification / Learning vector quantization

References

[1] 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

[2] Blake,I.F., Seroussi, G., Smart,N. , 1999. Elliptic Curves in Cryptography. Cambridge University Press. http://dx.doi.org/10.1017/CBO9781107360211

[3] 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

[4] de Mulder,E., Buysschaert, P., Ors,S.B. , , 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

[5] Duda,R.O., Hart,P.E., Stork,D.G. , 2011. Pattern Classification. John Wiley & Sons.

[6] 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

[7] Gersho,A., 1979. Asymptotically optimal block quantization. IEEE Trans. Inform. Theory, 25(4):373–380. http://dx.doi.org/10.1109/TIT.1979.1056067

[8] Haykin,S.S., 2009. Neural Networks and Learning Machines. Pearson Education, Upper Saddle River.

[9] 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

[10] Heyszl,J., Mangard, S., Heinz,B. , , 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

[11] Heyszl,J., Merli, D., Heinz,B. , , 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

[12] 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

[13] Koblitz,N., 1987. Elliptic curve cryptosystems. Math. Comput., 48(177):203–209. http://dx.doi.org/10.1090/S0025-5718-1987-0866109-5

[14] 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

[15] 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

[16] 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

[17] 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

[18] 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

[19] 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

[20] 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

[21] 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

[22] 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

[23] M�ntysalo,J., Torkkolay, K., Kohonen,T. , 1992. LVQbased speech recognition with high-dimensional context vectors. Int. Conf. on Spoken Language Processing, p.539–542.

[24] 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

[25] 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

[26] 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.

[27] 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

[28] Prouff,E., 2014. Constructive Side-Channel Analysis and Secure Design. Springer Berlin Heidelberg. http://dx.doi.org/10.1007/978-3-319-10175-0

[29] 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

[30] 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

[31] 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

[32] 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

[33] Wang,D., Ma,C., Zhang,Q., , 2013. Secure passwordbased remote user authentication scheme against smart card security breach. J. Networks, 8(1):148–155.

[34] Wang,D., He,D., Wang,P., , 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

[35] Wang,D., Wang,N., Wang,P., , 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

[36] 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

[37] 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

RIGHTS & PERMISSIONS

2017 Zhejiang University and Springer-Verlag Berlin Heidelberg