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Journal of Cryptographic Engineering
Published in: Journal of Cryptographic Engineering 2/2020

11-04-2019 | Regular Paper

Applications of machine learning techniques in side-channel attacks: a survey

Authors: Benjamin Hettwer, Stefan Gehrer, Tim Güneysu

Published in: Journal of Cryptographic Engineering | Issue 2/2020

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Abstract

With increasing expansion of the Internet of Things, embedded devices equipped with cryptographic modules become an important factor to protect sensitive data. Even though the employed algorithms in such devices are mathematically secure in theory, adversaries may still be able to compromise them by means of side-channel attacks. In power-based side-channel attacks, the instantaneous power consumption of the target is analyzed with statistical tools to draw conclusions about the secret keys that are used. There is a recent line of work that additionally makes use of techniques from the machine learning domain to attack cryptographic implementations. Since a complete review of this emerging field has not been done so far, this research aims to survey the current state of the art. We use a target-based classification to differentiate published work and drive general conclusions according to a common machine learning workflow. Furthermore, we outline the relationship between traditional power analysis techniques and machine learning-based attacks. This enables researchers to gain a better understanding of the topic in order to design new attack methods as well as potential countermeasures.
previous article Efficient modular operations using the adapted modular number system
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Appendix
Available only for authorised users
Footnotes
1
Note that a lower number not necessarily indicates a more powerful attack due to major differences in the attack methodology among the approaches.
 
Literature
1.
2.
Alva, J.A.V., Estrada, E.G.: A generalization of Shapiro–Wilk’s test for multivariate normality. Commun. Stat. Theory Methods 38(11), 1870–1883 (2009)MathSciNetMATH
3.
Banciu, V., Oswald, E., Whitnall, C.: Reliable information extraction for single trace attacks. In: Proceedings of the 2015 Design, Automation and Test in Europe Conference, DATE ’15, pp. 133–138. EDA Consortium, San Jose (2015)
4.
Bartkewitz, T.: Leakage prototype learning for profiled differential side-channel cryptanalysis. IEEE Trans. Comput. 65(6), 1761–1774 (2016)MathSciNetMATH
5.
Bartkewitz, T., Lemke-Rust, K.: Efficient template attacks based on probabilistic multi-class support vector machines. In: Mangard, S. (ed.) Smart Card Research and Advanced Applications: 11th International Conference, CARDIS 2012, Graz, Austria, November 28–30, 2012, Revised Selected Papers, pp. 263–276. Springer, Berlin (2013)
6.
Batina, L., Hogenboom, J., van Woudenberg, J.G.J.: Getting more from PCA: first results of using principal component analysis for extensive power analysis. In: Dunkelman, O. (ed.) Topics in Cryptology—CT-RSA 2012: The Cryptographers’ Track at the RSA Conference 2012, San Francisco, CA, USA, February 27–March 2, 2012. Proceedings, pp. 383–397. Springer, Berlin (2012)
7.
Bhasin, S., Bruneau, N., Danger, J.L., Guilley, S., Najm, Z.: Analysis and improvements of the DPA contest v4 implementation. In: Chakraborty, R.S., Matyas, V., Schaumont, P. (eds.) Security, Privacy, and Applied Cryptography Engineering: 4th International Conference, SPACE 2014, Pune, India, October 18–22, 2014. Proceedings, pp. 201–218. Springer, Cham (2014)
8.
Bilgin, B., Gierlichs, B., Nikova, S., Nikov, V., Rijmen, V.: Higher-order threshold implementations. In: Sarkar, P., Iwata, T. (eds.) Advances in Cryptology—ASIACRYPT 2014: 20th International Conference on the Theory and Application of Cryptology and Information Security, Kaoshiung, Taiwan, R.O.C., December 7–11, 2014, Proceedings, Part II, pp. 326–343. Springer, Berlin (2014)
9.
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press Inc, New York (1995)MATH
10.
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)MATH
11.
Brier, E., Clavier, C., Olivier, F.: Correlation power analysis with a leakage model. In: Joye, M., Quisquater, J.J. (eds.) Cryptographic Hardware and Embedded Systems—CHES 2004: 6th International Workshop Cambridge, MA, USA, August 11–13, 2004. Proceedings, pp. 16–29. Springer, Berlin (2004)
12.
Brier, E., Clavier, C., Olivier, F.: Improved template attacks. In: COSADE 2010—First International Workshop on Constructive Side-Channel Analysis and Secure Design (2010)
13.
Bruneau, N., Guilley, S., Heuser, A., Marion, D., Rioul, O.: Less is more. In: Güneysu, T., Handschuh, H. (eds.) Cryptographic Hardware and Embedded Systems-CHES 2015, pp. 22–41. Springer, Berlin (2015)
14.
Burman, S., Mukhopadhyay, D., Veezhinathan, K.: Lfsr based stream ciphers are vulnerable to power attacks. In: Srinathan, K., Rangan, C.P., Yung, M. (eds.) Progress in Cryptology—INDOCRYPT 2007: 8th International Conference on Cryptology in India, Chennai, India, December 9–13, 2007. Proceedings, pp. 384–392. Springer, Berlin (2007)
15.
Cagli, E., Dumas, C., Prouff, E.: Enhancing dimensionality reduction methods for side-channel attacks. In: Homma, N., Medwed, M. (eds.) Smart Card Research and Advanced Applications, pp. 15–33. Springer, Cham (2016)
16.
Cagli, E., Dumas, C., Prouff, E.: Convolutional neural networks with data augmentation against jitter-based countermeasures. In: Fischer, W., Homma, N. (eds.) Cryptographic Hardware and Embedded Systems-CHES 2017: 19th International Conference, Taipei, Taiwan, September 25–28, 2017, Proceedings, pp. 45–68. Springer, Cham (2017)
17.
Chakraborty, A.: Template attack on SPA and FA resistant implementation of montgomery ladder. IET Inf. Secur. 10(6), 245–251 (2016)
18.
Chakraborty, A., Mazumdar, B., Mukhopadhay, D.: Combined side-channel and fault analysis attack on protected grain family of stream ciphers. Cryptology ePrint Archive, Report 2015/602 (2015)
19.
Chakraborty, A., Mazumdar, B., Mukhopadhyay, D.: A practical DPA on grain v1 using LS-SVM. In: 2015 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), pp. 44–47 (2015)
20.
Chakraborty, A., Mukhopadhyay, D.: A practical template attack on mickey-128 2.0 using PSO generated IVS and LS-SVM. In: 2016 29th International Conference on VLSI Design and 2016 15th International Conference on Embedded Systems (VLSID), pp. 529–534 (2016)
21.
Chari, S., Rao, J.R., Rohatgi, P.: Template attacks. Cryptographic Hardware and Embedded Systems–CHES 2002: 4th International Workshop Redwood Shores. CA, USA, August 13–15, 2002 Revised Papers, pp. 13–28. Springer, Berlin (2003)
22.
Chou, J.W., Chu, M.H., Tsai, Y.L., Jin, Y., Cheng, C.M., Lin, S.D.: An unsupervised learning model to perform side channel attack. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14–17, 2013, Proceedings, Part I, pp. 414–425. Springer, Berlin (2013)
23.
Choudary, O., Kuhn, M.G.: Efficient template attacks. In: Francillon, A., Rohatgi, P. (eds.) Smart Card Research and Advanced Applications: 12th International Conference, CARDIS 2013, Berlin, Germany, November 27–29, 2013. Revised Selected Papers, pp. 253–270. Springer, Cham (2014)
24.
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)MATH
25.
Duan, L., Hongxin, Z., Qiang, L., Xinjie, Z., Pengfei, H.: Electromagnetic side-channel attack based on PSO directed acyclic graph SVM. J. China Univ. Posts Telecommun. 22(5), 10–15 (2015)
26.
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, New York (2000)MATH
27.
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995. MHS ’95, pp. 39–43 (1995)
28.
Eisenbarth, T., Paar, C., Weghenkel, B.: Building a side channel based disassembler. Transactions on Computational Science X: Special Issue on Security in Computing, Part I, pp. 78–99. Springer, Berlin (2010)
29.
30.
Gandolfi, K., Mourtel, C., Olivier, F.: Electromagnetic analysis: concrete results. In: Koç, Ç.K., Naccache, D., Paar, C. (eds.) Cryptographic Hardware and Embedded Systems-CHES 2001: Third International Workshop Paris, France, May 14–16, 2001 Proceedings, pp. 251–261. Springer, Berlin (2001)
31.
Genkin, D., Shamir, A., Tromer, E.: Acoustic cryptanalysis. J. Cryptol. 30(2), 392–443 (2017)MATH
32.
Gierlichs, B., Batina, L., Preneel, B., Verbauwhede, I.: Revisiting higher-order DPA attacks. In: Pieprzyk, J. (ed.) Topics in Cryptology—CT-RSA 2010: The Cryptographers’ Track at the RSA Conference 2010, San Francisco, CA, USA, March 1–5, 2010. Proceedings, pp. 221–234. Springer, Berlin (2010)
33.
Gierlichs, B., Batina, L., Tuyls, P., Preneel, B.: Mutual information analysis. In: Oswald, E., Rohatgi, P. (eds.) Cryptographic Hardware and Embedded Systems—CHES 2008: 10th International Workshop, Washington, DC, USA, August 10–13, 2008. Proceedings, pp. 426–442. Springer, Berlin (2008)
34.
Gierlichs, B., Lemke-Rust, K., Paar, C.: Templates vs. stochastic methods. In: Goubin, L., Matsui, M. (eds.) Cryptographic Hardware and Embedded Systems—CHES 2006: 8th International Workshop, Yokohama, Japan, October 10–13, 2006. Proceedings, pp. 15–29. Springer, Berlin (2006)
35.
Gilmore, R., Hanley, N., O’Neill, M.: Neural network based attack on a masked implementation of AES. In: 2015 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), pp. 106–111 (2015)
36.
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)MATH
37.
Güneysu, T., Moradi, A.: Generic side-channel countermeasures for reconfigurable devices. In: Preneel, B., Takagi, T. (eds.) Cryptographic Hardware and Embedded Systems—CHES 2011: 13th International Workshop, Nara, Japan, September 28–October 1, 2011. Proceedings, pp. 33–48. Springer, Berlin (2011)
38.
Guntur, H., Ishii, J., Satoh, A.: Side-channel attack user reference architecture board SAKURA-G. In: 2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE), pp. 271–274 (2014)
39.
Guo, G., Wang, H., Bell, D., Bi, Y., Greer, K.: KNN model-based approach in classification. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds.) On the Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE: OTM Confederated International Conferences, CoopIS, DOA, and ODBASE 2003, Catania, Sicily, Italy, November 3–7, 2003. Proceedings, pp. 986–996. Springer, Berlin (2003)
40.
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference and Prediction, 2nd edn. Springer, Berlin (2009)MATH
41.
He, H., Jaffe, J., Zou, L.: Side channel cryptanalysis using machine learning. Standford University, CS229 Fall Project (2012)
42.
Heuser, A., Picek, S., Guilley, S., Mentens, N.: Side-channel analysis of lightweight ciphers: does lightweight equal easy? Cryptology ePrint Archive, Report 2017/261. http://​eprint.​iacr.​org/​2017/​261 (2017). Accessed 19 Mar 2019
43.
Heuser, A., Zohner, M.: Intelligent machine homicide. In: Schindler, W., Huss, S.A. (eds.) Constructive Side-Channel Analysis and Secure Design: Third International Workshop, COSADE 2012, Darmstadt, Germany, May 3–4, 2012. Proceedings. Springer, Berlin (2012)
44.
Heyszl, J., Ibing, A., Mangard, S., De Santis, F., Sigl, G.: Clustering algorithms for non-profiled single-execution attacks on exponentiations. In: Francillon, A., Rohatgi, P. (eds.) Smart Card Research and Advanced Applications: 12th International Conference, CARDIS 2013, Berlin, Germany, November 27–29, 2013. Revised Selected Papers, pp. 79–93. Springer, Cham (2014)
45.
Heyszl, J., Merli, D., Heinz, B., De Santis, F., Sigl, G.: Strengths and limitations of high-resolution electromagnetic field measurements for side-channel analysis. In: Mangard, S. (ed.) Smart Card Research and Advanced Applications: 11th International Conference, CARDIS 2012, Graz, Austria, November 28–30, 2012. Revised Selected Papers, pp. 248–262. Springer, Berlin (2013)
46.
47.
Hoogvorst, P.: The variance power analysis. In: COSADE 2010—First International Workshop on Constructive Side-Channel Analysis and Secure Design (2010)
48.
Hospodar, G., Gierlichs, B., De Mulder, E., Verbauwhede, I., Vandewalle, J.: Machine learning in side-channel analysis: a first study. J. Cryptogr. Eng. 1(4), 293 (2011)
49.
Hou, S., Zhou, Y., Liu, H., Zhu, N.: Wavelet support vector machine algorithm in power analysis attacks. Radioengineering 26(3), 890–902 (2017)
50.
Huang, J., Zhou, Y., Liu, J.: Measuring the effectiveness of DPA attacks-from the perspective of distinguishers’ statistical characteristics. In: 2010 3rd International Conference on Computer Science and Information Technology, vol. 4, pp. 161–168 (2010)
51.
Jap, D., Breier, J.: Overview of machine learning based side-channel analysis methods. In: 2014 International Symposium on Integrated Circuits (ISIC), pp. 38–41 (2014)
52.
Jap, D., Stöttinger, M., Bhasin, S.: Support vector regression: exploiting machine learning techniques for leakage modeling. In: Proceedings of the Fourth Workshop on Hardware and Architectural Support for Security and Privacy, HASP ’15, pp. 2:1–2:8 (2015)
53.
Järvinen, K., Balasch, J.: Single-trace side-channel attacks on scalar multiplications with precomputations. In: Lemke-Rust, K., Tunstall, M. (eds.) Smart Card Research and Advanced Applications: 15th International Conference, CARDIS 2016, Cannes, France, November 7–9, 2016. Revised Selected Papers, pp. 137–155. Springer, Cham (2017)
54.
Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015)MathSciNetMATH
55.
Joye, M.: Elliptic curves and side-channel analysis. ST J. Syst. Res. 4, 17–21 (2003)
56.
Joye, M., Yen, S.M.: The Montgomery powering ladder. In: Kaliski, B.S., Koç, K., Paar, C. (eds.) Cryptographic Hardware and Embedded Systems—CHES 2002: 4th International Workshop Redwood Shores, CA, USA, August 13–15, 2002. Revised Papers, pp. 291–302. Springer, Berlin (2003)
57.
58.
Kocher, P., Jaffe, J., Jun, B.: Differential power analysis. Advances in Cryptology–CRYPTO’ 99: 19th Annual International Cryptology Conference Santa Barbara, California, USA, August 15–19, 1999. Proceedings, pp. 388–397. Springer, Berlin (1999)
59.
Kocher, P., Jaffe, J., Jun, B., Rohatgi, P.: Introduction to differential power analysis. J. Cryptogr. Eng. 1(1), 5–27 (2011)
60.
Kocher, P.C.: Timing attacks on implementations of Diffie-Hellman, RSA, DSS, and other systems. Advances in Cryptology–CRYPTO ’96: 16th Annual International Cryptology Conference Santa Barbara. California, USA August 18–22, 1996 Proceedings, pp. 104–113. Springer, Berlin (1996)
61.
Koeune, F., Standaert, F.X.: A tutorial on physical security and side-channel attacks. In: Aldini, A., Gorrieri, R., Martinelli, F. (eds.) Foundations of Security Analysis and Design III: FOSAD 2004/2005 Tutorial Lectures, pp. 78–108. Springer, Berlin (2005)MATH
62.
Kotsiantis, S.B.: Supervised machine learning: a review of classification techniques. In: Proceedings of the 2007 Conference on Emerging Artificial Intelligence Applications in Computer Engineering, pp. 3–24. IOS Press (2007)
63.
Langley, P.: Crafting papers on machine learning. In: Proceedings of the Seventeenth International Conference on Machine Learning (ICML), pp. 1207–1212 (2000)
64.
Lerman, L., Bontempi, G., Ben Taieb, S., Markowitch, O.: A time series approach for profiling attack. In: Gierlichs, B., Guilley, S., Mukhopadhyay, D. (eds.) Security, Privacy, and Applied Cryptography Engineering: Third International Conference, SPACE 2013, Kharagpur, India, October 19–23, 2013. Proceedings, pp. 75–94. Springer, Berlin (2013)
65.
Lerman, L., Bontempi, G., Markowitch, O.: Side channel attack: an approach based on machine learning. In: COSADE 2011—Second International Workshop on Constructive Side-Channel Analysis and Secure Design (2011)
66.
67.
Lerman, L., Bontempi, G., Markowitch, O.: A machine learning approach against a masked AES. J. Cryptogr. Eng. 5(2), 123–139 (2015)
68.
Lerman, L., Martinasek, Z., Markowitch, O.: Robust profiled attacks: should the adversary trust the dataset? IET Inf. Secur. 11(4), 188–194 (2017)
69.
Lerman, L., Medeiros, S.F., Bontempi, G., Markowitch, O.: A machine learning approach against a masked AES. In: Francillon, A., Rohatgi, P. (eds.) Smart Card Research and Advanced Applications: 12th International Conference, CARDIS 2013, Berlin, Germany, November 27–29, 2013. Revised Selected Papers, pp. 61–75. Springer, Berlin (2014)
70.
Lerman, L., Medeiros, S.F., Veshchikov, N., Meuter, C., Bontempi, G., Markowitch, O.: Semi-supervised template attack. In: Prouff, E. (ed.) Constructive Side-Channel Analysis and Secure Design: 4th International Workshop, COSADE 2013, Paris, France, March 6–8, 2013. Revised Selected Papers. Springer, Berlin (2013)
71.
Lerman, L., Poussier, R., Bontempi, G., Markowitch, O., Standaert, F.X.: Template attacks vs. machine learning revisited (and the curse of dimensionality in side-channel analysis). In: Mangard, S., Poschmann, A.Y. (eds.) Constructive Side-Channel Analysis and Secure Design: 6th International Workshop, COSADE 2015, Berlin, Germany, April 13–14, 2015. Revised Selected Papers, pp. 20–33. Springer, Cham (2015)
72.
73.
Liu, B., Ding, Z., Pan, Y., Li, J., Feng, H.: Side-channel attacks based on collaborative learning. Data Science: Third International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2017, Changsha, China, September 22–24, 2017. Proceedings, Part I, pp. 549–557. Springer, Singapore (2017)
74.
Liu, B., Feng, H., Yuan, Z., Gao, Y.: Learning to attack from electromagnetic emanation. In: 2012 6th Asia-Pacific Conference on Environmental Electromagnetics (CEEM), pp. 202–205 (2012)
75.
Liu, J., Zhou, Y., Han, Y., Li, J., Yang, S., Feng, D.: How to characterize side-channel leakages more accurately? In: Bao, F., Weng, J. (eds.) Information Security Practice and Experience: 7th International Conference, ISPEC 2011, Guangzhou, China, May 30–June 1, 2011. Proceedings, pp. 196–207. Springer, Berlin (2011)
76.
Maghrebi, H., Portigliatti, T., Prouff, E.: Breaking cryptographic implementations using deep learning techniques. In: Carlet, C., Hasan, M.A., Saraswat, V. (eds.) Security, Privacy, and Applied Cryptography Engineering: 6th International Conference, SPACE 2016, Hyderabad, India, December 14–18, 2016. Proceedings, pp. 3–26. Springer, Cham (2016)
77.
78.
Mangard, S.: A simple power-analysis (SPA) attack on implementations of the AES key expansion. In: Lee, P.J., Lim, C.H. (eds.) Information Security and Cryptology—ICISC 2002: 5th International Conference Seoul, Korea, November 28–29, 2002. Revised Papers, pp. 343–358. Springer, Berlin (2003)
79.
Mangard, S., Oswald, E., Popp, T.: Power Analysis Attacks: Revealing the Secrets of Smart Cards, 1st edn. Springer, New York (2010)MATH
80.
Martinasek, Z., Dzurenda, P., Malina, L.: Profiling power analysis attack based on MLP in DPA contest v4.2. In: 2016 39th International Conference on Telecommunications and Signal Processing (TSP), pp. 223–226 (2016)
81.
Martinasek, Z., Hajny, J., Malina, L.: Optimization of power analysis using neural network. In: Francillon, A., Rohatgi, P. (eds.) Smart Card Research and Advanced Applications: 12th International Conference, CARDIS 2013, Berlin, Germany, November 27–29, 2013. Revised Selected Papers, pp. 94–107. Springer, Cham (2014)
82.
Martinasek, Z., Malina, L.: Comparison of profiling power analysis attacks using templates and multi-layer perceptron network. Math. Methods Sci. Eng. (2014)
83.
Martinasek, Z., Malina, L., Trasy, K.: Profiling power analysis attack based on multi-layer perceptron network. In: Mastorakis, N., Bulucea, A., Tsekouras, G. (eds.) Computational Problems in Science and Engineering, pp. 317–339. Springer, Cham (2015)
84.
Martinasek, Z., Zeman, V., Malina, L., Martinasek, J.: k-Nearest neighbors algorithm in profiling power analysis attacks. Radioengineering 25(2), 365–382 (2016)
85.
Masci, J., Meier, U., Cireşan, D., Schmidhuber, J.: Stacked convolutional auto-encoders for hierarchical feature extraction. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds.) Artificial Neural Networks and Machine Learning—ICANN 2011: 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14–17, 2011. Proceedings, Part I, pp. 52–59. Springer, Berlin (2011)
86.
Mitchell, T.M.: Machine Learning, 1st edn. McGraw-Hill Inc, New York (1997)MATH
87.
Murphy, K.P.: Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge (2012)MATH
88.
Murthy, S.K.: Automatic construction of decision trees from data: a multi-disciplinary survey. Data Min. Knowl. Discov. 2(4), 345–389 (1998)
89.
Nassar, M., Souissi, Y., Guilley, S., Danger, J.L.: RSM: a small and fast countermeasure for AES, secure against 1st and 2nd-order zero-offset SCAs. In: 2012 Design, Automation Test in Europe Conference Exhibition (DATE), pp. 1173–1178 (2012)
90.
Okeya, K., Takagi, T.: The width-w NAF method provides small memory and fast elliptic scalar multiplications secure against side channel attacks. In: Joye, M. (ed.) Topics in Cryptology—CT-RSA 2003: The Cryptographers’ Track at the RSA Conference 2003 San Francisco, CA, USA, April 13–17, 2003. Proceedings, pp. 328–343. Springer, Berlin (2003)
91.
92.
Oswald, D., Paar, C.: Improving side-channel analysis with optimal linear transforms. In: Mangard, S. (ed.) Smart Card Research and Advanced Applications: 11th International Conference, CARDIS 2012, Graz, Austria, November 28–30, 2012. Revised Selected Papers, pp. 219–233. Springer, Berlin (2013)
93.
Özgen, E., Papachristodoulou, L., Batina, L.: Template attacks using classification algorithms. In: 2016 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), pp. 242–247 (2016)
94.
Perin, G., Imbert, L., Torres, L., Maurine, P.: Attacking randomized exponentiations using unsupervised learning. In: Prouff, E. (ed.) Constructive Side-Channel Analysis and Secure Design: 5th International Workshop, COSADE 2014, Paris, France, April 13–15, 2014. Revised Selected Papers, pp. 144–160. Springer, Cham (2014)
95.
Picek, S., Heuser, A., Guilley, S.: Template attack versus Bayes classifier. J. Cryptogr. Eng. 7(4), 343–351 (2017)
96.
97.
Picek, S., Heuser, A., Jovic, A., Legay, A.: Climbing down the hierarchy: hierarchical classification for machine learning side-channel attacks. In: Joye, M., Nitaj, A. (eds.) Progress in Cryptology—AFRICACRYPT 2017: 9th International Conference on Cryptology in Africa, Dakar, Senegal, May 24–26, 2017. Proceedings, pp. 61–78. Springer, Cham (2017)
98.
99.
Picek, S., Heuser, A., Jovic, A., Ludwig, S.A., Guilley, S., Jakobovic, D., Mentens, N.: Side-channel analysis and machine learning: A practical perspective. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 4095–4102 (2017)
100.
Powers, D.M.W.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J. Mach. Learn. Technol. 2, 37–63 (2011)
101.
Prouff, E., Strullu, R., Benadjila, R., Cagli, E., Dumas, C.: Study of deep learning techniques for side-channel analysis and introduction to ASCAD database. Cryptology ePrint Archive, Report 2018/053. https://​eprint.​iacr.​org/​2018/​053 (2018). Accessed 19 Mar 2019
102.
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
103.
Quisquater, J.J., Samyde, D.: Electromagnetic analysis (EMA): measures and counter-measures for smart cards. Smart Card Programming and Security: International Conference on Research in Smart Cards, E-smart 2001 Cannes, France, September 19–21, 2001. Proceedings, pp. 200–210. Springer, Berlin (2001)
104.
Raschka, S.: Linear discriminant analysis: bit by bit. https://​sebastianraschka​.​com/​Articles/​\(\backslash \)2014_python_lda.html. Accessed 27 Oct 2018
105.
Renauld, M., Standaert, F.X.: Algebraic side-channel attacks. In: Bao, F., Yung, M., Lin, D., Jing, J. (eds.) Information Security and Cryptology: 5th International Conference, Inscrypt 2009, Beijing, China, December 12–15, 2009. Revised Selected Papers, pp. 393–410. Springer, Berlin (2010)
106.
Rodriguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation forest: a new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1619–1630 (2006)
107.
Rührmair, U., Sehnke, F., Sölter, J., Dror, G., Devadas, S., Schmidhuber, J.: Modeling attacks on physical unclonable functions. In: Proceedings of the 17th ACM Conference on Computer and Communications Security, CCS ’10, pp. 237–249. ACM (2010)
108.
Rührmair, U., Xu, X., Sölter, J., Mahmoud, A., Majzoobi, M., Koushanfar, F., Burleson, W.: Efficient power and timing side channels for physical unclonable functions. In: Batina, L., Robshaw, M. (eds.) Cryptographic Hardware and Embedded Systems—CHES 2014: 16th International Workshop, Busan, South Korea, September 23–26, 2014. Proceedings, pp. 476–492. Springer, Berlin (2014)
109.
Saeedi, E., Hossain, M.S., Kong, Y.: Multi-class SVMs analysis of side-channel information of elliptic curve cryptosystem. In: Proceedings of the International Symposium on Performance Evaluation of Computer and Telecommunication Systems, Spects ’15, pp. 1–6. Society for Computer Simulation International, San Diego (2015)
110.
Saeedi, E., Hossain, M.S., Kong, Y.: Side-channel information characterisation based on cascade-forward back-propagation neural network. J. Electron. Test. 32(3), 345–356 (2016)
111.
Saeedi, E., Kong, Y.: Side channel information analysis based on machine learning. In: 2014 8th International Conference on Signal Processing and Communication Systems (ICSPCS), pp. 1–7 (2014)
112.
Saeedi, E., Kong, Y., Hossain, M.S.: Side-channel attacks and learning-vector quantization. Front. Inf. Technol. Electron. Eng. 18(4), 511–518 (2017)
113.
Saravanan, P., Kalpana, P.: A novel approach to attack smartcards using machine learning method. J. Sci. Ind. Res. (JSIR) 76, 95–99 (2017)
114.
Saravanan, P., Kalpana, P., Preethisri, V., Sneha, V.: Power analysis attack using neural networks with wavelet transform as pre-processor. In: 18th International Symposium on VLSI Design and Test, pp. 1–6 (2014)
115.
Sasdrich, P., Moradi, A., Mischke, O., Güneysu, T.: Achieving side-channel protection with dynamic logic reconfiguration on modern FPGAs. In: 2015 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), pp. 130–136 (2015). https://​doi.​org/​10.​1109/​HST.​2015.​7140251
116.
117.
Schindler, W., Lemke, K., Paar, C.: A stochastic model for differential side channel cryptanalysis. In: Rao, J.R., Sunar, B. (eds.) Cryptographic Hardware and Embedded Systems—CHES 2005: 7th International Workshop, Edinburgh, UK, August 29–September 1, 2005. Proceedings, pp. 30–46. Springer, Berlin (2005)
118.
Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)
119.
120.
Specht, R., Heyszl, J., Kleinsteuber, M., Sigl, G.: Improving non-profiled attacks on exponentiations based on clustering and extracting leakage from multi-channel high-resolution EM measurements. In: Mangard, S., Poschmann, A.Y. (eds.) Constructive Side-Channel Analysis and Secure Design: 6th International Workshop, COSADE 2015, Berlin, Germany, April 13–14, 2015. Revised Selected Papers, pp. 3–19. Springer, Cham (2015)
121.
Standaert, F.X., Archambeau, C.: Using subspace-based template attacks to compare and combine power and electromagnetic information leakages. In: Oswald, E., Rohatgi, P. (eds.) Cryptographic Hardware and Embedded Systems-CHES 2008, pp. 411–425. Springer, Berlin (2008)
122.
Standaert, F.X., Gierlichs, B., Verbauwhede, I.: Partition vs. comparison side-channel distinguishers: an empirical evaluation of statistical tests for univariate side-channel attacks against two unprotected CMOs devices. In: Lee, P.J., Cheon, J.H. (eds.) Information Security and Cryptology—ICISC 2008: 11th International Conference, Seoul, Korea, December 3–5, 2008. Revised Selected Papers, pp. 253–267. Springer, Berlin (2009)
123.
Standaert, F.X., Malkin, T.G., Yung, M.: A unified framework for the analysis of side-channel key recovery attacks. In: Joux, A. (ed.) Advances in Cryptology—EUROCRYPT 2009: 28th Annual International Conference on the Theory and Applications of Cryptographic Techniques, Cologne, Germany, April 26–30, 2009. Proceedings. Springer, Berlin (2009)
124.
125.
126.
127.
128.
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press Inc, Orlando (2008)MATH
129.
Thillard, A., Prouff, E., Roche, T.: Success through confidence: evaluating the effectiveness of a side-channel attack. In: Bertoni, G., Coron, J.S. (eds.) Cryptographic Hardware and Embedded Systems—CHES 2013: 15th International Workshop, Santa Barbara, CA, USA, August 20–23, 2013. Proceedings, pp. 21–36. Springer, Berlin (2013)
130.
131.
van Gestel, T., Suykens, J.A., Baesens, B., Viaene, S., Vanthienen, J., Dedene, G., de Moor, B., Vandewalle, J.: Benchmarking least squares support vector machine classifiers. Mach. Learn. 54(1), 5–32 (2004)MATH
132.
Webb, G.I., Boughton, J.R., Wang, Z.: Not so naive Bayes: aggregating one-dependence estimators. Mach. Learn. 58(1), 5–24 (2005)MATH
133.
Whitnall, C., Oswald, E.: Robust profiling for DPA-style attacks. In: Güneysu, T., Handschuh, H. (eds.) Cryptographic Hardware and Embedded Systems—CHES 2015: 17th International Workshop, Saint-Malo, France, September 13–16, 2015. Proceedings, pp. 3–21. Springer, Berlin (2015)
134.
Whitnall, C., Oswald, E., Mather, L.: An exploration of the Kolmogorov–Smirnov test as a competitor to mutual information analysis. In: Prouff, E. (ed.) Smart Card Research and Advanced Applications, pp. 234–251. Springer, Berlin (2011)
135.
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evolut. Comput. 1(1), 67–82 (1997)
136.
Yang, S., Zhou, Y., Liu, J., Chen, D.: Back propagation neural network based leakage characterization for practical security analysis of cryptographic implementations. In: Kim, H. (ed.) Information Security and Cryptology—ICISC 2011: 14th International Conference, Seoul, Korea, November 30–December 2, 2011. Revised Selected Papers, pp. 169–185. Springer, Berlin (2012)
137.
Zadeh, A.A., Heys, H.M.: Simple power analysis applied to nonlinear feedback shift registers. IET Inf. Secur. 8(3), 188–198 (2014)
138.
Zdenek, M., Zeman, V.: Innovative method of the power analysis. Radioengineering 22(2), 586–594 (2013)
139.
Zeng, Z., Gu, D., Liu, J., Guo, Z.: An improved side-channel attack based on support vector machine. In: 2014 Tenth International Conference on Computational Intelligence and Security, pp. 676–680 (2014)
140.
Zhang, H., Han, G., Li, J.: Wavelet transform-principal component analysis in electromagnetic attack. In: 2015 7th Asia-Pacific Conference on Environmental Electromagnetics (CEEM), pp. 420–423 (2015)
141.
Zheng, Y., Zhou, Y., Yu, Z., Hu, C., Zhang, H.: How to compare selections of points of interest for side-channel distinguishers in practice? In: Hui, L.C.K., Qing, S.H., Shi, E., Yiu, S.M. (eds.) Information and Communications Security: 16th International Conference, ICICS 2014, Hong Kong, China, December 16–17, 2014. Revised Selected Papers, pp. 200–214. Springer, Cham (2015)
142.
Zhuang, L., Zhou, F., Tygar, J.D.: Keyboard acoustic emanations revisited. ACM Trans. Inf. Syst. Secur. 13(1), 3:1–3:26 (2009)
143.
Zohner, M., Kasper, M., Stöttinger, M., Huss, S.A.: Side channel analysis of the SHA-3 finalists. In: Proceedings of the Conference on Design, Automation and Test in Europe, DATE ’12, pp. 1012–1017. EDA Consortium, San Jose (2012)
144.
Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. ArXiv e-prints (2016)
Metadata
Title
Applications of machine learning techniques in side-channel attacks: a survey
Authors
Benjamin Hettwer
Stefan Gehrer
Tim Güneysu
Publication date
11-04-2019
Publisher
Springer Berlin Heidelberg
Published in
Journal of Cryptographic Engineering / Issue 2/2020
Print ISSN: 2190-8508
Electronic ISSN: 2190-8516
DOI
https://doi.org/10.1007/s13389-019-00212-8

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