Confidence Value: A Novel Evaluation Index of Side-Channel Attack

  • Xiaomin Cai
  • Shijie KuangEmail author
  • Gao Shen
  • Renfa Li
  • Shaoqing Li
  • Xing Hu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1146)


The side-channel attacks (SCAs) use the correlation between the power leakage information and the key to implement the attack process. The result of SCAs has a certain probability. If guessing an 8-bit key, there is a probability of 1/256 that the key will be guessed coincidentally, resulting in false positive. Therefore, the reliability of result key also needs an index to measure. Thereby, this paper proposes a novel evaluation index based on confidence value (CV). The CV of result key is divided three levels, low false positive, medium false positive and high false positive. CV provides a new reference index for the designers, suppliers and users of cryptographic devices to evaluate the security of devices.


Cryptographic device Side-channel attacks Confidence value False positive FCM clustering 



The project is supported in part by the National Natural Science Foundation of China under Grant (61702172, 61672217, 61832018) and the National Key Research and Development Plan of China under Grant 2016YFB0200405.


  1. 1.
    Chung, S., Yu, C., Lee, S., Chang, H., Lee, C.: An improved DPA countermeasure based on uniform distribution random power generator for IoT applications. IEEE Trans. Circ. Syst. I Regul. Pap. 64, 2522–2531 (2017)CrossRefGoogle Scholar
  2. 2.
    Gebotys, C.H., White, B.A.: A phase substitution technique for DEMA of embedded cryptographic systems. In: Information Technology, pp. 868–869 (2007)Google Scholar
  3. 3.
    Ding, G., et al.: Electromagnetic emanations of the ICs. In: 2007 4th IEEE International Symposium on Electromagnetic Compatibility Proceeding, pp. 303–305. IEEE Press, Qingdao (2007)Google Scholar
  4. 4.
    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). Scholar
  5. 5.
    Gandolfi, K., Mourtel, C., Olivier, F.: Electromagnetic analysis: concrete results. In: Koç, Ç.K., Naccache, D., Paar, C. (eds.) CHES 2001. LNCS, vol. 2162, pp. 251–261. Springer, Heidelberg (2001). Scholar
  6. 6.
    Ors, S., Gurkaynak, F., Oswald, E., Preneel, B.: Power-analysis attack on an ASIC AES implementation. In: Proceedings of ITCC, Las Vegas, pp. 5–7 (2004)Google Scholar
  7. 7.
    Quisquater, J.-J., Samyde, D.: Electro magnetic analysis (EMA): measures and counter-measures for smart cards. In: Attali, I., Jensen, T. (eds.) E-smart 2001. LNCS, vol. 2140, pp. 200–210. Springer, Heidelberg (2001). Scholar
  8. 8.
    Kocher, P., Jaffe, J., Jun, B.: Differential power analysis. In: Wiener, M. (ed.) CRYPTO 1999. LNCS, vol. 1666, pp. 388–397. Springer, Heidelberg (1999). Scholar
  9. 9.
    Oswald, E., Mangard, S., Herbst, C., Tillich, S.: Practical second-order DPA attacks for masked smart card implementations of block ciphers. In: Pointcheval, D. (ed.) CT-RSA 2006. LNCS, vol. 3860, pp. 192–207. Springer, Heidelberg (2006). Scholar
  10. 10.
    Akkar, M.-L., Giraud, C.: An implementation of DES and AES, secure against some attacks. In: Koç, Ç.K., Naccache, D., Paar, C. (eds.) CHES 2001. LNCS, vol. 2162, pp. 309–318. Springer, Heidelberg (2001). Scholar
  11. 11.
    Fouque, P.-A., Kunz-Jacques, S., Martinet, G., Muller, F., Valette, F.: Power attack on small RSA public exponent. In: Goubin, L., Matsui, M. (eds.) CHES 2006. LNCS, vol. 4249, pp. 339–353. Springer, Heidelberg (2006). Scholar
  12. 12.
    Messerges, T.S., Dabbish, E.A., Sloan, R.H.: Power analysis attacks of modular exponentiation in smartcards. In: Koç, Ç.K., Paar, C. (eds.) CHES 1999. LNCS, vol. 1717, pp. 144–157. Springer, Heidelberg (1999). Scholar
  13. 13.
    Akkar, M.-L., Bevan, R., Dischamp, P., Moyart, D.: Power analysis, what is now possible…. In: Okamoto, T. (ed.) ASIACRYPT 2000. LNCS, vol. 1976, pp. 489–502. Springer, Heidelberg (2000). Scholar
  14. 14.
    Cao, Y., et al.: On the negative effects of trend noise and its applications in side-channel cryptanalysis. Chin. J. Electron. 23(2), 366–370 (2014)Google Scholar
  15. 15.
    Chari, S., Jutla, C., R., Rao, J., et al: A cautionary note regarding evaluation of AES candidates on smart-cards (1999)Google Scholar
  16. 16.
    Levi, I., Fish, A., Keren, O.: CPA secured data-dependent delay-assignment methodology. IEEE Trans. Very Large-Scale Integr. (VLSI) Syst. 25, 608–620 (2017)CrossRefGoogle Scholar
  17. 17.
    Shan, W., Zhang, S., He, Y.: Machine learning based side-channel-attack countermeasure with hamming-distance redistribution and its application on advanced encryption standard. Electron. Lett. 53(14), 926–928 (2017)CrossRefGoogle Scholar
  18. 18.
    Moradi, A., Guilley, S., Heuser, A.: Detecting hidden leakages. In: Boureanu, I., Owesarski, P., Vaudenay, S. (eds.) ACNS 2014. LNCS, vol. 8479, pp. 324–342. Springer, Cham (2014). Scholar
  19. 19.
    Hamdi, T., Ghith, A., Fayala, F.: Characterization of drape profile using Fuzzy-C-Mean (FCM) method. Fibers Polym. 18, 1401–1407 (2017)CrossRefGoogle Scholar
  20. 20.
    McGrath, M.: Python. In: Easy Steps (2014). Accessed 9 June 2019
  21. 21.
    Shen, G., Zhang, Q., Tang, Y., et al.: Power analysis attack based on FCM clustering algorithm. In: The 14th International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2018. EIGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Xiaomin Cai
    • 1
  • Shijie Kuang
    • 1
    Email author
  • Gao Shen
    • 2
  • Renfa Li
    • 1
  • Shaoqing Li
    • 2
  • Xing Hu
    • 2
  1. 1.College of Computer Science and Electronic EngineeringHunan UniversityChangshaPeople’s Republic of China
  2. 2.College of ComputerNational University of Defense TechnologyChangshaPeople’s Republic of China

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