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Front. Inform. Technol. Electron. Eng.  2017, Vol. 18 Issue (4): 511-518    DOI: 10.1631/FITEE.1500460
Research Articles     
Side-channel attacks and learning-vector quantization
Ehsan Saeedi, Yinan Kong, Md. Selim Hossain
Department of Engineering, Macquarie University, Sydney, Australia
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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.

Key wordsSide-channel attacks      Elliptic curve cryptography      Multi-class classification      Learning vector quantization     
Received: 19 December 2015      Published: 12 April 2017
CLC:  TP309  
Cite this article:

Ehsan Saeedi, Yinan Kong, Md. Selim Hossain. Side-channel attacks and learning-vector quantization. Front. Inform. Technol. Electron. Eng., 2017, 18(4): 511-518.

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http://www.zjujournals.com/xueshu/fitee/10.1631/FITEE.1500460     OR     http://www.zjujournals.com/xueshu/fitee/Y2017/V18/I4/511


边信道攻击和学习向量量化

概要:尽管加密算法已得到改进,加密系统的安全性仍然是密码系统设计者关注的重点。边信道攻击可利用加密系统的物理漏洞来获取秘密信息。目前提出的多种边信道信息分析方法中,机器学习被认为是一种有前景的方法。基于神经网络的机器学习可获得指令标志(功耗与电磁辐射),并自动识别。本文对椭圆曲线加密(Elliptic curve cryptography, ECC)的现场可编程门阵列(field-programmable gate array, FPGA)实现展开了新的实验研究,探讨了基于学习向量量化(Learning vector quantization, LVQ)神经网络的边信道信息表征的效率。LVQ作为多类分类器的主要特点是它具有学习复杂非线性输入-输出关系、使用顺序训练程序和适应数据的能力。实验结果表明基于LVQ的多类分类是边信道数据表征的强大且有前景的方法。

关键词: 边信道攻击,  椭圆曲线加密,  多类分类,  学习向量量化 
[1]   Bartkewitz, T., Lemke-Rust, K., 2013. Efficient template attacks based on probabilistic multi-class support vector machines. LNCS, 7771:263-276.
doi: 10.1007/978-3-642-37288-9_18
[2]   Blake, I.F., Seroussi, G., Smart, N., 1999. Elliptic Curves in Cryptography. Cambridge University Press.
doi: 10.1017/CBO9781107360211
[3]   Cybenko, G., 1989. Approximation by superpositions of a sigmoidal function. Math. Contr. Signals Syst., 2(4):303-314.
doi: 10.1007/BF02551274
[4]   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.
doi: 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).
doi: 10.1515/bmte.1992.37.12.303
[7]   Gersho, A., 1979. Asymptotically optimal block quantization. IEEE Trans. Inform. Theory, 25(4):373-380.
doi: 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.
doi: 10.1007/978-3-642-29912-4_18
[10]   Heyszl, J., Mangard, S., Heinz, B., et al., 2012a. Localized electromagnetic analysis of cryptographic implementations. Cryptographers’ Track at the RSA Conf., p.231-244.
doi: 10.1007/978-3-642-27954-6_15
[11]   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.
doi: 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.
doi: 10.1007/3-540-36400-5_11
[13]   Koblitz, N., 1987. Elliptic curve cryptosystems. Math. Comput., 48(177):203-209.
doi: 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.
doi: 10.1007/3-540-48405-1_25
[15]   Kohonen, T., 1988. An introduction to neural computing. Neur. Networks, 1(1):3-16.
doi: 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.
doi: 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.
doi: 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.
doi: 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.
doi: 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.
doi: 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.
doi: 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.
doi: 10.1007/978-0-387-38162-6
[23]   Mäntysalo, J., Torkkolay, K., Kohonen, T., 1992. LVQ-based 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.
doi: 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.
doi: 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.
doi: 10.1016/0925-2312(94)00071-9
[28]   Prouff, E., 2014. Constructive Side-Channel Analysis and Secure Design. Springer Berlin Heidelberg.
doi: 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.
doi: 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.
doi: 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.
doi: 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.
doi: 10.1007/978-3-319-27659-5_16
[33]   Wang, D., Ma, C., Zhang, Q., et al., 2013. Secure password-based remote user authentication scheme against smart card security breach. J. Networks, 8(1):148-155.
[34]   Wang, D., He, D., Wang, P., et al., 2015a. Anonymous two-factor authentication in distributed systems: certain goals are beyond attainment. IEEE Trans. Depend. Sec. Comput., 12(4):428-442.
doi: 10.1109/TDSC.2014.2355850
[35]   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.
doi: 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.
doi: 10.1631/FITEE.1400232
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