Climbing Down the Hierarchy: Hierarchical Classification for Machine Learning Side-Channel Attacks
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.
KeywordsSide-channel attacks Profiled scenario Machine learning techniques Hierarchical classification Hierarchical attack Structured attack
S. Picek was supported in part by Croatian Science Foundation under the project IP-2014-09-4882.
- 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)Google Scholar
- 10.TELECOM ParisTech SEN research group: DPA Contest. 2nd edn. (2009–2010). http://www.DPAcontest.org/v2/
- 11.Xilinx: Virtex-5 libraries guide for HDL designs. http://www.xilinx.com/support/documentation/sw_manuals/xilinx14_4/virtex5_hdl.pdf
- 12.TELECOM ParisTech SEN research group: DPA Contest. 4th edn. (2013–2014). http://www.DPAcontest.org/v4/
- 13.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)Google Scholar
- 15.Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)Google Scholar
- 16.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)Google Scholar
- 20.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)Google Scholar
- 22.Powers, D.M.W.: Evaluation: from precision, recall and F-factor to ROC, informedness, markedness and correlation (2007)Google Scholar
- 23.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 CrossRefGoogle Scholar