Abstract
Machine learning techniques represent a powerful paradigm in side-channel analysis, but they come with a price. Selecting the appropriate algorithm as well as the parameters can sometimes be a difficult task. Nevertheless, the results obtained usually justify such an effort. However, a large part of those results use simplification of the data relation and in fact do not consider allthe available information. In this paper, we analyze the hierarchical relation between the data and propose a novel hierarchical classification approach for side-channel analysis. With this technique, we are able to introduce two new attacks for machine learning side-channel analysis: Hierarchical attack and Structured attack. Our results show that both attacks can outperform machine learning techniques using the traditional approach as well as the template attack regarding accuracy. To support our claims, we give extensive experimental results and discuss the necessary conditions to conduct such attacks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Note that, an attacker could reveal the secret key with only one trace if it corresponds to HW 0 or 8, which occurs with a probability of \(\frac{2}{256}\).
- 2.
For simplicity we assume that one key chunk is of the same size as one intermediate state chunk, however, this study can easily be extended for other scenarios as given e.g. in DES.
- 3.
See e.g., in the hall of fame on [10].
References
Chari, S., Rao, J.R., Rohatgi, P.: Template attacks. In: Kaliski, B.S., Koç, K., Paar, C. (eds.) CHES 2002. LNCS, vol. 2523, pp. 13–28. Springer, Heidelberg (2003). doi:10.1007/3-540-36400-5_3
Schindler, W., Lemke, K., Paar, C.: A stochastic model for differential side channel cryptanalysis. In: Rao, J.R., Sunar, B. (eds.) CHES 2005. LNCS, vol. 3659, pp. 30–46. Springer, Heidelberg (2005). doi:10.1007/11545262_3
Lerman, L., Bontempi, G., Markowitch, O.: Side channel attack: an approach based on machine learning. In: Second International Workshop on Constructive SideChannel Analysis and Secure Design, Center for Advanced Security Research Darmstadt, pp. 29–41 (2011)
Hospodar, G., Gierlichs, B., De Mulder, E., Verbauwhede, I., Vandewalle, J.: Machine learning in side-channel analysis: a first study. J. Cryptographic Eng. 1, 293–302 (2011). doi:10.1007/s13389-011-0023-x
Heuser, A., Zohner, M.: Intelligent machine homicide. In: Schindler, W., Huss, S.A. (eds.) COSADE 2012. LNCS, vol. 7275, pp. 249–264. Springer, Heidelberg (2012). doi:10.1007/978-3-642-29912-4_18
Lerman, L., Bontempi, G., Markowitch, O.: The bias-variance decomposition in profiled attacks. J. Cryptographic Eng. 5(4), 255–267 (2015)
Lerman, L., Bontempi, G., Markowitch, O.: Power analysis attack: an approach based on machine learning. IJACT 3(2), 97–115 (2014)
Lerman, L., Medeiros, S.F., Bontempi, G., Markowitch, O.: A machine learning approach against a masked AES. In: Francillon, A., Rohatgi, P. (eds.) CARDIS 2013. LNCS, vol. 8419, pp. 61–75. Springer, Cham (2014). doi:10.1007/978-3-319-08302-5_5
Heuser, A., Kasper, M., Schindler, W., Stöttinger, M.: A new difference method for side-channel analysis with high-dimensional leakage models. In: Dunkelman, O. (ed.) CT-RSA 2012. LNCS, vol. 7178, pp. 365–382. Springer, Heidelberg (2012). doi:10.1007/978-3-642-27954-6_23
TELECOM ParisTech SEN research group: DPA Contest. 2nd edn. (2009–2010). http://www.DPAcontest.org/v2/
Xilinx: Virtex-5 libraries guide for HDL designs. http://www.xilinx.com/support/documentation/sw_manuals/xilinx14_4/virtex5_hdl.pdf
TELECOM ParisTech SEN research group: DPA Contest. 4th edn. (2013–2014). http://www.DPAcontest.org/v4/
de Almendra Freitas, C.O., Oliveira, L.S., Aires, S.B.K., Bortolozzi, F.: Metaclasses and zoning mechanism applied to handwriting recognition. J. UCS 14(2), 211–223 (2008)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2), 131–163 (1997)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: Shavlik, J. (ed.) Fifteenth International Conference on Machine Learning, pp. 144–151. Morgan Kaufmann (1998)
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)
Kuncheva, L.I., RodrÃguez, J.J.: An experimental study on rotation forest ensembles. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 459–468. Springer, Heidelberg (2007). doi:10.1007/978-3-540-72523-7_46
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Schoelkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning. MIT Press (1998)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Powers, D.M.W.: Evaluation: from precision, recall and F-factor to ROC, informedness, markedness and correlation (2007)
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.) COSADE 2014. LNCS, vol. 9064, pp. 20–33. Springer, Cham (2015). doi:10.1007/978-3-319-21476-4_2
Choudary, O., Kuhn, M.G.: Efficient template attacks. In: Francillon, A., Rohatgi, P. (eds.) CARDIS 2013. LNCS, vol. 8419, pp. 253–270. Springer, Cham (2014). doi:10.1007/978-3-319-08302-5_17
Whitnall, C., Oswald, E.: Robust profiling for DPA-style attacks. In: Güneysu, T., Handschuh, H. (eds.) CHES 2015. LNCS, vol. 9293, pp. 3–21. Springer, Heidelberg (2015). doi:10.1007/978-3-662-48324-4_1
Acknowledgments
S. Picek was supported in part by Croatian Science Foundation under the project IP-2014-09-4882.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Picek, S., Heuser, A., Jovic, A., Legay, A. (2017). Climbing Down the Hierarchy: Hierarchical Classification for Machine Learning Side-Channel Attacks. In: Joye, M., Nitaj, A. (eds) Progress in Cryptology - AFRICACRYPT 2017. AFRICACRYPT 2017. Lecture Notes in Computer Science(), vol 10239. Springer, Cham. https://doi.org/10.1007/978-3-319-57339-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-57339-7_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57338-0
Online ISBN: 978-3-319-57339-7
eBook Packages: Computer ScienceComputer Science (R0)