Abstract
The Power analysis attack is an effective method of attacking encryption devices for leakage of side-channel information. CPA (Correlation Power Analysis) is a common method. The traditional method of Power Analysis Attack, which is only one-byte key, is analyzed in one attack and repeats multiple operations to obtain the whole secret key. In this way, a successful attack needs more power curves. In this paper, a new attack method is proposed to select the optimal secret key group through the Ant Colony Algorithm and attack all the bytes of the secret key simultaneously. It can greatly eliminate the influence of the channel noise and improve the efficiency of the attack. To prove the effectiveness of this new method, the AES algorithm as an example is implemented on the MEGA16 microcontroller. The power consumption curve of the AES algorithm with a fixed secret key and random plaintext is collected, and the power consumption is analyzed separately by the original method and the new method. As a result, the success rate of the original method is only 10.981% when using 4000 power curves; however, the new one is up to 100%, which is increased by 89.019%. When the power curves do not exceed 3000, the success rate of the original method is zero. However, the success rate of the new method can reach 34.375% even if only 1500 power curves are used. The new method is more effective than the original one. Being affected by parameters, the attack time of the new method is not consistent but much less than the original method.
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References
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
Alioto, M., Poli, M., Rocchi, S.: Differential power analysis attacks to precharged buses: a general analysis for symmetric-key cryptographic algorithms. Dependab. Secure Comput. IEEE Trans. 7(3), 226–239 (2009)
Oswald, D., Paar, C.: Improving side-channel analysis with optimal linear transforms. In: Mangard, S. (ed.) CARDIS 2012. LNCS, vol. 7771, pp. 219–233. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37288-9_15
Satoh, R., Matsushima, D., Shiozaki, M., et al.: Subkey driven hybrid power analysis attack in frequency domain against cryptographic LSIs and its evaluation. IEEJ Trans. Electron. Inf. Syst. 133(7), 1322–1330 (2013)
Kim, Y., Ko, H.: Using principal component analysis for practical biasing of power traces to improve power analysis attacks. In: Lee, H.-S., Han, D.-G. (eds.) ICISC 2013. LNCS, vol. 8565, pp. 109–120. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12160-4_7
Lerman, L., Bontempi, G., Markowitch, O.: Power analysis attack: an approach based on machine learning. Int. J. Appl. Cryptogr. 3(2), 97–115 (2014)
Shanmugam, D., Selvam, R., Annadurai, S.: Differential power analysis attack on SIMON and LED block ciphers. In: Chakraborty, R.S., Matyas, V., Schaumont, P. (eds.) SPACE 2014. LNCS, vol. 8804, pp. 110–125. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12060-7_8
Merino Del Pozo, S., Standaert, F.-X.: Blind source separation from single measurements using singular spectrum analysis. In: Güneysu, T., Handschuh, H. (eds.) CHES 2015. LNCS, vol. 9293, pp. 42–59. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48324-4_3
Guo, L., Li, Q., Wang, L., et al.: A differential power analysis attack on dynamic password token based on SM3 algorithm. International Conference on Information Science & Electronic Technology (2015)
Mahanta, H.J., Azad, A.K., Khan, A.K.: Power analysis attack: a vulnerability to smart card security. In: International Conference on Signal Processing & Communication Engineering Systems. IEEE (2015)
Guo, L., Wang, L., Liu, D., et al.: A chosen - plaintext differential power analysis attack on HMAC - SM3. In: 2015 11th International Conference on Computational Intelligence and Security (CIS). IEEE (2016)
Masoumi, M., Habibi, P., Dehghan, A., Jadidi, M., Yousefi, L.: Efficient implementation of power analysis attack resistant advanced encryption standard algorithm on side-channel attack standard evaluation board. Int. J. Internet Technol. Secur. Trans. 6(3), 203 (2016). https://doi.org/10.1504/IJITST.2016.080392
Yoshikawa, M., Nozaki, Y., Asahi, K.: Multiple rounds aware power analysis attack for a lightweight cipher SIMECK. In: IEEE Second International Conference on Big Data Computing Service & Applications. IEEE (2016)
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
Chakraborty, A., Mondal, A., Srivastava, A.: Correlation power analysis attack against STT-MRAM based cyptosystems. In: IEEE International Symposium on Hardware Oriented Security & Trust. IEEE (2017)
Wiemers, A., Klein, D.: Entropy reduction for the correlation-enhanced power analysis collision attack. In: Proceedings of the 13th International Workshop on Security, IWSEC 2018, Sendai, Japan, 3–5 Sep 2018 (2018)
Kim, J., Picek, S., Heuser, A., Bhasin, S., Hanjalic, A.: Make some noise unleashing the power of convolutional neural networks for profiled side-channel analysis. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2019(3), 148–179 (2019). 430
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(2), 163–188 (2019)
Cai, X., Li, R., Kuang, S., Tan, J.: An energy trace compression method for differential power analysis attack. IEEE Access 8, 89084–89092 (2020)
Duan, X., Chen, D., Fan, X., Li, X., Ding, D., Li, Y.: Research and implementation on power analysis attacks for unbalanced data. Secur. Commun. Netw. 2020, 1–10 (2020)
Acknowledgments
This research was supported by the High-tech discipline construction funds of China (No. 20210032Z0401, No. 20210033Z0402) and the open project of Key Laboratory of cryptography and information security in Guangxi, China (No. GCIS201912).
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Duan, X., Li, Y., Tong, J., Li, X., He, S., Zhang, P. (2021). Research of CPA Attack Methods Based on Ant Colony Algorithm. In: Garcia-Alfaro, J., Li, S., Poovendran, R., Debar, H., Yung, M. (eds) Security and Privacy in Communication Networks. SecureComm 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 398. Springer, Cham. https://doi.org/10.1007/978-3-030-90019-9_14
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