Advertisement

Profiling Power Analysis Attack Based on Multi-layer Perceptron Network

  • Zdenek MartinasekEmail author
  • Lukas Malina
  • Krisztina Trasy
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 343)

Abstract

In 2013, an innovative method of power analysis was presented in Martinasek and Zeman (Radioengineering 22(2), IF 0.687, 2013) and Martinasek et al. (Smart Card Research and Advanced Applications. Lecture Notes in Computer Science. Springer International Publishing, New York, 2014). Realized experiments proved that the proposed method based on Multi-Layer Perceptron (MLP) can provide almost 100 % success rate. This description based on the first-order success rate is not appropriate enough. Moreover, the above mentioned works contain other lacks: the MLP has not been compared with other well-known attacks, an adversary uses too many points of power trace and a general description of the MLP method was not provided. In this paper, we eliminate these weaknesses by introducing the first fair comparison of power analysis attacks based on the MLP and templates. The comparison is accomplished by using the identical data sets, number of interesting points and guessing entropy as a metric. The first data set created contains the power traces of an unprotected AES implementation in order to classify the secret key stored. The second and third data sets were created independently from public available power traces corresponding to a masked AES implementation (DPA Contest v4). Secret offset is revealed depending on the number of interesting points and power traces in this experiment. Moreover, we create a general description of the MLP attack.

Keywords

Power analysis MLP Machine learning Template attack Comparison 

Notes

Acknowledgements

Research described in this paper was financed by the National Sustainability Program under grant LO1401. For the research, infrastructure of the SIX Center was used.

References

  1. 1.
    Federal Information Processing Standards Publication (FIPS 197). Advanced Encryption Standard (AES) (2001)Google Scholar
  2. 2.
    Oswald, M.E., Mangard, S., Herbst, C., Tillich, S.: Practical second-order dpa attacks for masked smart card implementations of block ciphers. In: Pointcheval, D. (ed.) Topics in Cryptology - CT-RSA 2006. Lecture Notes in Computer Science, vol. 3860, pp. 192–207. Springer, Berlin (2006)CrossRefGoogle Scholar
  3. 3.
    Raval, N., Bansod, G., Pisharoty, N.: Implementation of efficient bit permutation box for embedded security. WSEAS Trans. Comput. 13(1), 442–451 (2014)Google Scholar
  4. 4.
    Herbst, C., Oswald, E., Mangard, S.: An AES smart card implementation resistant to power analysis attacks. In: Second International Conference on Applied Cryptography and Network Security (ACNS 2006). Lecture Notes in Computer Science, vol. 3989, 239–252. Springer, Heidelberg (2006)Google Scholar
  5. 5.
    5. Joye, M., Olivier, F.: Side-channel analysis. In: van Tilborg, H.C.A., Jajodia, S. (eds.) Encyclopedia of Cryptography and Security, 2nd edn., pp. 1198–1204. Springer (2011). ISBN: 978-1-4419-5905-8Google Scholar
  6. 6.
    Fouque, P.A., Kunz-Jacques, S., Martinet, G., Muller, F., Valette, F.: Power attack on small rsa public exponent. In: 8th International Workshop Cryptographic Hardware and Embedded Systems - CHES 2006. Lecture Notes in Computer Science, vol. 4249, pp. 339–353. Springer, Berlin (2006)Google Scholar
  7. 7.
    Choudary, O., Kuhn, M.G.: Efficient template attacks. In: Smart Card Research and Advanced Applications - 12th International Conference, CARDIS 2013, Berlin, 27-29 November 2013, pp. 253–270. Revised Selected Papers. http://dblp.uni-trier.de/rec/bibtex/conf/cardis/ChoudaryK13 (2013)
  8. 8.
    Liu, M., Shien, W.: On the security of yoon and yoo’s biometrics remote user authentication scheme. WSEAS Trans. Inf. Sci. Appl. 11(1), 94–104 (2014)Google Scholar
  9. 9.
    Mangard, S., Oswald, E., Popp, T.: Power Analysis Attacks: Revealing the Secrets of Smart Cards (Advances in Information Security). Springer, New York, Secaucus (2007)Google Scholar
  10. 10.
    Kocher, P.C., Jaffe, J., Jun, B.: Differential power analysis. In: CRYPTO ’99: Proceedings of the 19th Annual International Cryptology Conference on Advances in Cryptology, pp. 388–397. Springer, London (1999)Google Scholar
  11. 11.
    Coron, J.S., Goubin, L.: On boolean and arithmetic masking against differential power analysis. In: Proceedings of the Second International Workshop on Cryptographic Hardware and Embedded Systems (CHES ’00), pp. 231–237. Springer, London (2000)Google Scholar
  12. 12.
    Nassar, M., Souissi, Y., Guilley, S., Danger, J.L.: RSM: A small and fast countermeasure for AES, secure against 1st and 2nd-order zero-offset scas. In: DATE, pp. 1173–1178 (2012)Google Scholar
  13. 13.
    Muresan, R., Vahedi, H., Zhanrong, Y., Gregori, S.: Power-smart system-on-chip architecture for embedded cryptosystems. In: Proceedings of the 3rd IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS ’05), pp. 184–189. ACM, New York (2005)Google Scholar
  14. 14.
    Mesquita, D., Techer, J.D., Torres, L., Sassatelli, G., Cambon, G., Robert, M., Moraes, F.: Current mask generation: A transistor level security against dpa attacks. In: SBCCI, pp. 115–120 (2005)Google Scholar
  15. 15.
    Amin, A., Alsomani, T.: Elliptic curve cryptoprocessor with hierarchical security. WSEAS Trans. Circuits Syst. 13(1), 135–145 (2014)Google Scholar
  16. 16.
    Chari, S., Rao, J.R., Rohatgi, P.: Template attacks. In: CHES, pp. 13–28 (2002)Google Scholar
  17. 17.
    Hanley, N., Tunstall, M., Marnane, W.P.: Using templates to distinguish multiplications from squaring operations. Int. J. Inf. Secur. 10(4), 255–266 (2011)CrossRefGoogle Scholar
  18. 18.
    Bar, M., Drexler, H., Pulkus, J.: Improved template attacks. In: COSADE 2010 - First International Workshop on Constructive Side-Channel Analysis and Secure Design, pp. 81–89 (2010)Google Scholar
  19. 19.
    Brier, E., Clavier, C., Olivier, F.: Correlation power analysis with a leakage model. In: CHES, pp. 16–29 (2004)Google Scholar
  20. 20.
    20. Quisquater, J.J., Samyde, D.: Automatic code recognition for smart cards using a kohonen neural network. In: Proceedings of the 5th Conference on Smart Card Research and Advanced Application Conference (CARDIS’02), Berkeley, vol. 5. http://dblp.uni-trier.de/rec/bibtex/conf/cardis/QuisquaterS02 (2002)
  21. 21.
    Kur, J., Smolka, T., Svenda, P.: Improving resiliency of java card code against power analysis. In: Mikulaska kryptobesidka, Sbornik prispevku, pp. 29–39 (2009)Google Scholar
  22. 22.
    Martinasek, Z., Macha, T., Zeman, V.: Classifier of power side channel. In: Proceedings of NIMT2010, September 2010Google Scholar
  23. 23.
    Yang, S., Zhou, Y., Liu, J., Chen, D.: Back propagation neural network based leakage characterization for practical security analysis of cryptographic implementations. In: Proceedings of the 14th International Conference on Information Security and Cryptology (ICISC ’11), pp. 169–185. Springer, Berlin (2012)Google Scholar
  24. 24.
    Lerman, L., Bontempi, G., Markowitch, O.: Side channel attack: An approach based on machine learningn. In: COSADE 2011 - Second International Workshop on Constructive Side-Channel Analysis and Secure Design, pp. 29–41 (2011)Google Scholar
  25. 25.
    Liran, L., Gianluca, B., Olivier, M.: Power analysis attack: An approach based on machine learning. Int. J. Appl. Cryptogr. 3(2), 97–115 (2013)Google Scholar
  26. 26.
    Hospodar, G., Gierlichs, B., Mulder, E.D., Verbauwhede, I., Vandewalle, J.: Machine learning in side-channel analysis: A first study. J. Cryptogr. Eng. 1(4), 293–302 (2011)CrossRefGoogle Scholar
  27. 27.
    Hospodar, G., Mulder, E., Gierlichs, B., Vandewalle, J., Verbauwhede, I.: Least squares support vector machines for side-channel analysis. In: COSADE 2011 - Second International Workshop on Constructive Side-Channel Analysis and Secure Design, pp. 293–302 (2011)Google Scholar
  28. 28.
    Heuser, A., Zohner, M.: Intelligent machine homicide - breaking cryptographic devices using support vector machines. In: COSADE, pp. 249–264 (2012)Google Scholar
  29. 29.
    Bartkewitz, T., Lemke-Rust, K.: Efficient template attacks based on probabilistic multi-class support vector machines. In: Proceedings of the 11th International Conference on Smart Card Research and Advanced Applications (CARDIS ’12), pp. 263–276. Springer, Berlin (2013)Google Scholar
  30. 30.
    Lerman, L., Bontempi, G., Taieb, S.B., Markowitch, O.: A time series approach for profiling attack. In: Gierlichs, B., Guilley, S., Mukhopadhyay, D. (eds.) SPACE. Lecture Notes in Computer Science, vol. 8204, pp. 75–94. Springer, Berlin (2013)Google Scholar
  31. 31.
    Lerman, L., Medeiros, S., Bontempi, G., Markowitch, O.: A machine learning approach against a masked AES. In: Francillon, A., Rohatgi, P. (eds.) Smart Card Research and Advanced Applications. Lecture Notes in Computer Science, pp. 61–75. Springer International Publishing, Berlin (2014)Google Scholar
  32. 32.
    Martinasek, Z., Zeman, V.: Innovative method of the power analysis. Radioengineering 22(2), IF 0.687 (2013)Google Scholar
  33. 33.
    Martinasek, Z., Hajny, J., Malina, L.: Optimization of power analysis using neural network. In: Francillon, A., Rohatgi, P. (eds.) Smart Card Research and Advanced Applications. Lecture Notes in Computer Science, pp. 94–107. Springer International Publishing, Heidelberg (2014)Google Scholar
  34. 34.
    Standaert, F.X., Malkin, T., Yung, M.: A unified framework for the analysis of side-channel key recovery attacks. In: EUROCRYPT, pp. 443–461 (2009)Google Scholar
  35. 35.
    Martinasek, Z., Clupek, V., Krisztina, T.: General scheme of differential power analysis. In: 2013 36th International Conference on Telecommunications and Signal Processing (TSP), pp. 358–362 (2013)Google Scholar
  36. 36.
    Martinasek, Z., Zeman, V., Sysel, P., Trasy, K.: Near electromagnetic field measurement of microprocessor. Przegl. Elektrotechniczny 89(2a), 203–207 (2013)Google Scholar
  37. 37.
    Guilleyho, S.: DPA contest v4. http://www.dpacontest.org/v4/index.php (2013)
  38. 38.
    Nabney, I.T.: NETLAB: Algorithms for Pattern Recognition. Advances in Pattern Recognition. Springer, New York (2002)Google Scholar
  39. 39.
    Kasabov, N.K.: Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, 1st edn. MIT Press, Cambridge (1996)zbMATHGoogle Scholar
  40. 40.
    Archambeau, C., Peeters, E., Standaert, F.X., Quisquater, J.J.: Template attacks in principal subspaces. In: CHES, pp. 1–14 (2006)Google Scholar
  41. 41.
    Jain, L.C., Martin, N.M.: Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications, 1st edn. CRC Press, Boca Raton (1998)Google Scholar
  42. 42.
    Moradi, A., Guilley, S., Heuser, A.: Detecting hidden leakages. Cryptology ePrint Archive, Report 2013/842. http://eprint.iacr.org/ (2013)

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Zdenek Martinasek
    • 1
    Email author
  • Lukas Malina
    • 1
  • Krisztina Trasy
    • 2
  1. 1.Department of TelecommunicationsBrno University of TechnologyBrnoCzech Republic
  2. 2.Department of Garden and Landscape ArchitectureMendel University in BrnoLedniceCzech Republic

Personalised recommendations