Abstract
It has been proven that side-channel analysis such as differential computation/fault analysis can break white-box implementations without reverse engineering efforts. In 2020, Sun et al. proposed noisy rounds as a countermeasure to mitigate the side-channel attacks on white-box block ciphers. The principle is to desynchronize the computation traces of cryptographic implementations by introducing several redundant round functions. In this paper, we propose a multi-label classification method and three deep-learning models (CNN, RNN, and CRNN) to predict the locations of the obfuscated rounds. The experimental results show that the obfuscation of noisy rounds also could not be identified by the deep-learning model. However, the RNN is more effective than the CNN and CRNN with fewer time costs. Subsequently, we investigate the influence of specific components such as the key, affine masking, and transformation matrix on round obfuscation recognition. The extended experiments demonstrate that without the transformation matrix, the deep learning models can successfully distinguish the noisy rounds.
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References
Alpirez Bock, E., Brzuska, C., Michiels, W., Treff, A.: On the ineffectiveness of internal encodings - revisiting the DCA attack on white-box cryptography. In: Preneel, B., Vercauteren, F. (eds.) ACNS 2018. LNCS, vol. 10892, pp. 103–120. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93387-0_6
Banik, S., Bogdanov, A., Isobe, T., Jepsen, M.B.: Analysis of software countermeasures for whitebox encryption. Cryptology ePrint Archive (2017)
Billet, O., Gilbert, H., Ech-Chatbi, C.: Cryptanalysis of a white box AES implementation. In: Handschuh, H., Hasan, M.A. (eds.) SAC 2004. LNCS, vol. 3357, pp. 227–240. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30564-4_16
Bos, J.W., Hubain, C., Michiels, W., Teuwen, P.: Differential computation analysis: hiding your white-box designs is not enough. In: Gierlichs, B., Poschmann, A.Y. (eds.) CHES 2016. LNCS, vol. 9813, pp. 215–236. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-53140-2_11
Bringer, J., Chabanne, H., Dottax, E.: White box cryptography: another attempt. Cryptology ePrint Archive (2006)
Chow, S., Eisen, P., Johnson, H., van Oorschot, P.C.: A white-box DES implementation for DRM applications. In: Feigenbaum, J. (ed.) DRM 2002. LNCS, vol. 2696, pp. 1–15. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-44993-5_1
Chow, S., Eisen, P., Johnson, H., Van Oorschot, P.C.: White-box cryptography and an AES implementation. In: Nyberg, K., Heys, H. (eds.) SAC 2002. LNCS, vol. 2595, pp. 250–270. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36492-7_17
Gierlichs, B., Schmidt, J.-M., Tunstall, M.: Infective computation and dummy rounds: fault protection for block ciphers without check-before-output. In: Hevia, A., Neven, G. (eds.) LATINCRYPT 2012. LNCS, vol. 7533, pp. 305–321. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33481-8_17
Karroumi, M.: Protecting white-box AES with dual ciphers. In: Rhee, K.-H., Nyang, D.H. (eds.) ICISC 2010. LNCS, vol. 6829, pp. 278–291. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24209-0_19
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, NIPS 2012, Red Hook, NY, USA, vol. 1, pp. 1097–1105. Curran Associates Inc. (2012)
Lee, J.H., Han, D.-G.: DLDDO: deep learning to detect dummy operations. In: You, I. (ed.) WISA 2020. LNCS, vol. 12583, pp. 73–85. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-65299-9_6
Lepoint, T., Rivain, M., De Mulder, Y., Roelse, P., Preneel, B.: Two attacks on a white-box AES implementation. In: Lange, T., Lauter, K., Lisoněk, P. (eds.) SAC 2013. LNCS, vol. 8282, pp. 265–285. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-43414-7_14
Lerman, L., Bontempi, G., Markowitch, O.: A machine learning approach against a masked AES. J. Cryptogr. Eng. 5(2), 123–139 (2015). https://doi.org/10.1007/s13389-014-0089-3
Maghrebi, H., Portigliatti, T., Prouff, E.: Breaking cryptographic implementations using deep learning techniques. In: Carlet, C., Hasan, M.A., Saraswat, V. (eds.) SPACE 2016. LNCS, vol. 10076, pp. 3–26. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49445-6_1
Muir, J.A.: A tutorial on white-box AES. In: Kranakis, E. (ed.) Advances in Network Analysis and Its Applications. MATHINDUSTRY, vol. 18, pp. 209–229. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-30904-5_9
De Mulder, Y., Roelse, P., Preneel, B.: Cryptanalysis of the Xiao – Lai white-box AES implementation. In: Knudsen, L.R., Wu, H. (eds.) SAC 2012. LNCS, vol. 7707, pp. 34–49. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-35999-6_3
De Mulder, Y., Wyseur, B., Preneel, B.: Cryptanalysis of a perturbated white-box AES implementation. In: Gong, G., Gupta, K.C. (eds.) INDOCRYPT 2010. LNCS, vol. 6498, pp. 292–310. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17401-8_21
Picek, S., Samiotis, I.P., Kim, J., Heuser, A., Bhasin, S., Legay, A.: On the performance of convolutional neural networks for side-channel analysis. In: Chattopadhyay, A., Rebeiro, C., Yarom, Y. (eds.) SPACE 2018. LNCS, vol. 11348, pp. 157–176. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05072-6_10
Rivain, M., Wang, J.: Analysis and improvement of differential computation attacks against internally-encoded white-box implementations. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2019(2), 225–255 (2019)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation, pp. 318–362. MIT Press, Cambridge (1986)
Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. CoRR abs/1507.05717 (2015). http://arxiv.org/abs/1507.05717
Sun, T., Tang, G., Wu, X., Mao, Z., Gong, Z.: A noisyrounds-based white-box AES implementation and corresponding differential fault analysis. J. Cryptol. Res. 7(3), 342–57 (2020)
Tang, Y., Gong, Z., Sun, T., Chen, J., Liu, Z.: WBMatrix: an optimized matrix library for white-box block cipher implementations. IEEE Trans. Comput. 71(12), 3375–88 (2022)
Tingting, L., Xuejia, L.: Efficient attack to white-box SMS4 implementation. J. Softw. 24(9), 2238–2249 (2013)
Wu, L., Picek, S.: Remove some noise: on pre-processing of side-channel measurements with autoencoders. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2020(4), 389–415 (2020)
Xiao, Y., Lai, X.: A secure implementation of white-box AES. In: 2009 2nd International Conference on Computer Science and Its Applications, pp. 1–6. IEEE (2009)
Xiao, Y., Lai, X.: White-box cryptography and implementations of SMS4. In: Proceedings of the 2009 CACR Annual Meeting, Guangzhou, China, pp. 24–34 (2009)
Yueyu, Z., Dong, X., Jie, C.: Analysis and improvement of white box SM4. J. Electron. Inf. Technol. 43, 1–11 (2021)
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)
Zeyad, M., Maghrebi, H., Alessio, D., Batteux, B.: Another look on bucketing attack to defeat white-box implementations. In: Polian, I., Stöttinger, M. (eds.) COSADE 2019. LNCS, vol. 11421, pp. 99–117. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16350-1_7
Zhang, Y., Xu, D., Cai, Z., Chen, J.: Analysis of the mean difference of intermediate-values in a white box SM4. J. Xidian Univ. 49(1), 111–120 (2022)
Acknowledgments
This work was supported by the National Natural Science Foundation of China (62072192), National Defense Technology 173 Basic Improvement Project (2121-JCJQ-JJ-0931), National Cryptography Development Fund (MMJJ20180206), Guangdong Basic and Applied Basic Research Foundation (2022A1515140090), the Research Project of Science and Technology Plan of Guangzhou (No. 2023B 03J0172).
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Deng, T. et al. (2023). A Deep-Learning Approach for Predicting Round Obfuscation in White-Box Block Ciphers. In: Zhou, J., et al. Applied Cryptography and Network Security Workshops. ACNS 2023. Lecture Notes in Computer Science, vol 13907. Springer, Cham. https://doi.org/10.1007/978-3-031-41181-6_23
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