Abstract
Side-channel analysis is an important part of the security evaluations of hardware components and more specifically of those that include cryptographic algorithms. Profiling attacks are among the most powerful attacks as they assume the attacker has access to a clone device of the one under attack. Using the clone device allows the attacker to make a profile of physical leakages linked to the execution of algorithms. This work focuses on the characteristics of this profile and the information that can be extracted from its application to the attack traces. More specifically, looking at unsuccessful attacks, it shows that by carefully ordering the attack traces used and limiting their number, better results can be achieved with the same profile. Using this method allows us to consider the classical attack method, i.e., where the traces are randomly ordered, as the worst-case scenario. The best-case scenario is when the attacker is able to successfully order and select the best attack traces. A method for identifying efficient ordering when using deep learning models as profiles is also provided. A new loss function “scoring loss” is dedicated to training machine learning models that give a score to the attack prediction and the score can be used to order the predictions.
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References
Chari, S., Rao, J.R., Rohatgi, P.: Template attacks. In: Cryptographic Hardware and Embedded Systems - CHES 2002, pp. 13–28. Springer, Berlin, Heidelberg (2003)
Zaid, G., Bossuet, L., Habrard, A., Venelli, A.: Methodology for efficient CNN architectures in profiling attacks. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2020(1), 1–36 (2020)
Robissout, D., Bossuet, L., Habrard, A., Grosso, V.: Improving deep learning networks for profiled side-channel analysis using performance improvement techniques. J. Emerg. Technol. Comput. Syst. (2021). https://doi.org/10.1145/3453162
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, pp. 443–461. Springer, Berlin, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01001-9_26
Hoang, A.-T., Hanley, N., O’Neill, M.: Plaintext: a missing feature for enhancing the power of deep learning in side-channel analysis? breaking multiple layers of side-channel countermeasures. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2020(4), 49–85 (2020). https://doi.org/10.13154/tches.v2020.i4.49-85
Acharya, R.Y., Ganji, F., Forte, D.: Information theory-based evolution of neural networks for side-channel analysis. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2023(1), 401–437 (2022). https://doi.org/10.46586/tches.v2023.i1.401-437
Benadjila, R., Prouff, E., Strullu, R., Cagli, E., Dumas, C.: Deep learning for side-channel analysis and introduction to ascad database. J. Cryptogr. Eng. 10, 163–188 (2019). https://doi.org/10.1007/s13389-019-00220-8
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.: Learning to rank using gradient descent. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 89–96 (2005)
Burges, C., Ragno, R., Le, Q.: Learning to rank with nonsmooth cost functions. Adv. Neural. Inf. Process. Syst. 19, 193–200 (2006)
Chen, W., Liu, T.-Y., Lan, Y., Ma, Z.-M., Li, H.: Ranking measures and loss functions in learning to rank. Adv. Neural. Inf. Process. Syst. 22, 315–323 (2009)
Lv, Y., Moon, T., Kolari, P., Zheng, Z., Wang, X., Chang, Y.: Learning to model relatedness for news recommendation. In: Proceedings of the 20th International Conference on World Wide Web, pp. 57–66 (2011)
Xiang, B., Jiang, D., Pei, J., Sun, X., Chen, E., Li, H.: Context-aware ranking in web search. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 451–458 (2010)
Covington, P., Adams, J., Sargin, E.: Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems. RecSys ’16, pp. 191–198. Association for Computing Machinery, (2016). https://doi.org/10.1145/2959100.2959190
Wu, Q., Burges, C.J., Svore, K.M., Gao, J.: Adapting boosting for information retrieval measures. Inf. Retrieval 13(3), 254–270 (2010)
Li, H.: Learning to rank for information retrieval and natural language processing. Synth. Lect. Hum. Lang. Technol. 7(3), 1–121 (2014)
Cao, Z., Qin, T., Liu, T.-Y., Tsai, M.-F., Li, H.: Learning to rank: from pairwise approach to listwise approach. In: Proceedings of the 24th International Conference on Machine Learning, pp. 129–136 (2007)
Xia, F., Liu, T.-Y., Wang, J., Zhang, W., Li, H.: Listwise approach to learning to rank: theory and algorithm. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1192–1199 (2008)
Zaid, G., Bossuet, L., Dassance, F., Habrard, A., Venelli, A.: Ranking loss: maximizing the success rate in deep learning side-channel analysis. IACR Trans. Cryptogr. Hardw. Embed. Syst. pp 25–55 (2021)
Bromley, J., Bentz, J.W., Bottou, L., Guyon, I., LeCun, Y., Moore, C., Säckinger, E., Shah, R.: Signature verification using a siamese time delay neural network. Int. J. Pattern Recognit Artif Intell. 7(04), 669–688 (1993)
Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: Fully-convolutional siamese networks for object tracking. In: Hua, G., Jégou, H. (eds.) Computer Vision - ECCV 2016 Workshops, pp. 850–865. Springer, Cham (2016)
Guo, Q., Feng, W., Zhou, C., Huang, R., Wan, L., Wang, S.: Learning dynamic siamese network for visual object tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1763–1771 (2017)
Gleize, M., Shnarch, E., Choshen, L., Dankin, L., Moshkowich, G., Aharonov, R., Slonim, N.: Are you convinced? choosing the more convincing evidence with a siamese network. arXiv:1907.08971 (2019)
Qin, T., Liu, T.-Y., Li, H.: A general approximation framework for direct optimization of information retrieval measures. Inf. Retr. 13(4), 375–397 (2010)
Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017)
Azouaoui, M., Poussier, R., Standaert, F.-X., Verneuil, V.: Key enumeration from the adversarial viewpoint. In: Belaïd, S., Güneysu, T. (eds.) Smart Card Research and Advanced Applications, pp. 252–267. Springer, Cham (2020)
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D.R. wrote the main manuscript text and prepared the figures. All authors reviewed the manuscript. A native English speaker was hired to correct the English of the manuscript.
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Robissout, D., Bossuet, L. & Habrard, A. Scoring the predictions: a way to improve profiling side-channel attacks. J Cryptogr Eng (2024). https://doi.org/10.1007/s13389-024-00346-4
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DOI: https://doi.org/10.1007/s13389-024-00346-4