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Convolutional Neural Network Based Side-Channel Attacks with Customized Filters

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Information and Communications Security (ICICS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11999))

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Abstract

Deep learning is progressively gaining attention as a powerful tool for conducting profiling side-channel attacks. In particular, convolutional neural network (CNN) is one of the mostly employed learning techniques in the context of side-channel analysis. The first layer of a standard CNN always performs a set of convolutions between the input and some finite impulse response filters. In this work, we substitute the standard filter by a customized filter borrowed from the domain of speaker recognition due to the resemblance between the power traces and speech signals. In contrast to standard filters, the new filter only depends on parameters with a clear physical meaning, where only low and high cutoff frequencies are learned from the training data. Experimental results obtained from public datasets show that the side-channel attacks based on CNNs equipped with this new filter are more effective and robust than attacks based on standard CNNs. The results of this work open new perspective and encourage further research on the effect of the filters of the CNN-based side-channel attacks.

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References

  1. Deep Learning Tutorials. http://deeplearning.net/reading-list/tutorials/

  2. DPA contest v2. http://www.dpacontest.org/v2/

  3. DPA contest v4. http://www.dpacontest.org/v4/rsm_doc.php

  4. Keras library. https://keras.io/

  5. Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems. CoRR abs/1603.04467 (2016)

    Google Scholar 

  6. Agrawal, D., Archambeault, B., Rao, J.R., Rohatgi, P.: The EM side-channel(s). In: Cryptographic Hardware and Embedded Systems - CHES 2002, 4th International Workshop, Redwood Shores, CA, USA, 13–15 August 2002, Revised Papers, pp. 29–45 (2002)

    Google Scholar 

  7. ANSSI: ASCAD database (2019). https://github.com/ANSSI-FR/ASCAD

  8. Bartkewitz, T., Lemke-Rust, K.: Efficient template attacks based on probabilistic multi-class support vector machines. In: Mangard, S. (ed.) CARDIS 2012. LNCS, vol. 7771, pp. 263–276. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37288-9_18

    Chapter  Google Scholar 

  9. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  10. Boureau, Y., Ponce, J., LeCun, Y.: A theoretical analysis of feature pooling in visual recognition. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), Haifa, Israel, 21–24 June 2010, pp. 111–118 (2010)

    Google Scholar 

  11. Brier, E., Clavier, C., Olivier, F.: Correlation power analysis with a leakage model. In: Joye, M., Quisquater, J.-J. (eds.) CHES 2004. LNCS, vol. 3156, pp. 16–29. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28632-5_2

    Chapter  Google Scholar 

  12. Cagli, E., Dumas, C., Prouff, E.: Convolutional neural networks with data augmentation against jitter-based countermeasures. In: Fischer, W., Homma, N. (eds.) CHES 2017. LNCS, vol. 10529, pp. 45–68. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66787-4_3

    Chapter  Google Scholar 

  13. 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). https://doi.org/10.1007/3-540-36400-5_3

    Chapter  Google Scholar 

  14. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  15. Doget, J., Prouff, E., Rivain, M., Standaert, F.: Univariate side channel attacks and leakage modeling. J. Cryptographic Eng. 1(2), 123–144 (2011)

    Article  Google Scholar 

  16. Gierlichs, B., Batina, L., Tuyls, P., Preneel, B.: Mutual information analysis. In: Oswald, E., Rohatgi, P. (eds.) CHES 2008. LNCS, vol. 5154, pp. 426–442. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85053-3_27

    Chapter  Google Scholar 

  17. Gilmore, R., Hanley, N., O’Neill, M.: Neural network based attack on a masked implementation of AES. In: IEEE International Symposium on Hardware Oriented Security and Trust, HOST 2015, Washington, DC, USA, 5–7 May 2015, pp. 106–111 (2015)

    Google Scholar 

  18. Hettwer, B., Gehrer, S., Güneysu, T.: Profiled power analysis attacks using convolutional neural networks with domain knowledge. In: Selected Areas in Cryptography - SAC 2018–25th International Conference, Calgary, AB, Canada, 15–17 August 2018, Revised Selected Papers, pp. 479–498 (2018)

    Google Scholar 

  19. 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). https://doi.org/10.1007/978-3-642-29912-4_18

    Chapter  Google Scholar 

  20. Hospodar, G., Gierlichs, B., Mulder, E.D., Verbauwhede, I., Vandewalle, J.: Machine learning in side-channel analysis: a first study. J. Cryptographic Eng. 1(4), 293–302 (2011)

    Article  Google Scholar 

  21. Kocher, P.C.: Timing attacks on implementations of Diffie-Hellman, RSA, DSS, and other systems. In: Koblitz, N. (ed.) CRYPTO 1996. LNCS, vol. 1109, pp. 104–113. Springer, Heidelberg (1996). https://doi.org/10.1007/3-540-68697-5_9

    Chapter  Google Scholar 

  22. Kocher, P., Jaffe, J., Jun, B.: Differential power analysis. In: Wiener, M. (ed.) CRYPTO 1999. LNCS, vol. 1666, pp. 388–397. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48405-1_25

    Chapter  Google Scholar 

  23. LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series. In: The Handbook of Brain Theory and Neural Networks, pp. 255–258 (1998)

    Google Scholar 

  24. LeCun, Y., Bengio, Y., Hinton, G.E.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  25. Lerman, L., Bontempi, G., Markowitch, O.: Power analysis attack: an approach based on machine learning. IJACT 3(2), 97–115 (2014)

    Article  MathSciNet  Google Scholar 

  26. Lerman, L., Medeiros, S.F., Bontempi, G., Markowitch, O.: A machine learning approach against a masked AES. In: Smart Card Research and Advanced Applications - 12th International Conference, CARDIS 2013, Berlin, Germany, 27–29 November 2013, Revised Selected Papers, pp. 61–75 (2013)

    Google Scholar 

  27. 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). https://doi.org/10.1007/978-3-319-21476-4_2

    Chapter  Google Scholar 

  28. 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

    Chapter  Google Scholar 

  29. O’Shea, K., Nash, R.: An introduction to convolutional neural networks. CoRR abs/1511.08458 (2015)

    Google Scholar 

  30. Prouff, E., Strullu, R., Benadjila, R., Cagli, E., Dumas, C.: Study of deep learning techniques for side-channel analysis and introduction to ASCAD database. IACR Cryptol. ePrint Archive 2018, 53 (2018)

    Google Scholar 

  31. Ravanelli, M., Bengio, Y.: Speaker recognition from raw waveform with SincNet. In: 2018 IEEE Spoken Language Technology Workshop, SLT 2018, Athens, Greece, 18–21 December 2018, pp. 1021–1028 (2018)

    Google Scholar 

  32. Rokach, L., Maimon, O.Z.: Data Mining with Decision Trees: Theory and Applications, vol. 69. World scientific, Singapore (2008)

    MATH  Google Scholar 

  33. Schindler, W.: Advanced stochastic methods in side channel analysis on block ciphers in the presence of masking. J. Math. Cryptol. 2(3), 291–310 (2008)

    Article  MathSciNet  Google Scholar 

  34. 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). https://doi.org/10.1007/11545262_3

    Chapter  Google Scholar 

  35. Timon, B.: Non-profiled deep learning-based side-channel attacks with sensitivity analysis. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2019(2), 107–131 (2019)

    Article  Google Scholar 

  36. Weston, J., Watkins, C.: Multi-class support vector machines (1998)

    Google Scholar 

  37. Yang, G., Li, H., Ming, J., Zhou, Y.: Convolutional neural network based side-channel attacks in time-frequency representations. In: International Conference on Smart Card Research and Advanced Applications, pp. 1–17 (2018)

    Google Scholar 

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Acknowledgements

The authors thank the anonymous reviewers for many helpful comments. The work is supported by the National Key R&D Program of China (Grant No. 2018YFA0704704), the Chinese Major Program of National Cryptography Development Foundation (Grant No. MMJJ20180102), the National Natural Science Foundation of China (61772519, 61732021, 61802400, 61802399), and the Youth Innovation Promotion Association of Chinese Academy of Sciences.

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Authors and Affiliations

  1. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Man Wei, Danping Shi, Siwei Sun, Peng Wang & Lei Hu

  2. Data Assurance and Communication Security Research Center, Chinese Academy of Sciences, Beijing, China

    Man Wei, Danping Shi, Siwei Sun, Peng Wang & Lei Hu

  3. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Man Wei, Danping Shi, Siwei Sun, Peng Wang & Lei Hu

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  1. Man Wei
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  2. Danping Shi
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  3. Siwei Sun
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  5. Lei Hu
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Correspondence to Man Wei .

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Editors and Affiliations

  1. Singapore University of Technology and Design, Singapore, Singapore

    Jianying Zhou

  2. The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Xiapu Luo

  3. Peking University, Beijing, China

    Qingni Shen

  4. Institute of Information Engineering, Beijing, China

    Zhen Xu

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Wei, M., Shi, D., Sun, S., Wang, P., Hu, L. (2020). Convolutional Neural Network Based Side-Channel Attacks with Customized Filters. In: Zhou, J., Luo, X., Shen, Q., Xu, Z. (eds) Information and Communications Security. ICICS 2019. Lecture Notes in Computer Science(), vol 11999. Springer, Cham. https://doi.org/10.1007/978-3-030-41579-2_46

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  • DOI: https://doi.org/10.1007/978-3-030-41579-2_46

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  • Online ISBN: 978-3-030-41579-2

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