A Novel Points of Interest Selection Method For SVM-based Profiled Attacks
DOI:
https://doi.org/10.54654/isj.v2i12.117Keywords:
side channel attack, profiled attack, points of interest, variational mode decomposition.Tóm tắt
Abstract—Currently, one of the most powerful side channel attacks (SCA) is profiled attack. Machine learning algorithms, for example support vector machine (SVM), are currently used to improve the effectiveness of the attack. One issue of using SVM-based profiled attack is extracting points of interest (POIs), or features from power traces. Our work proposes a novel method for POIs selection of power traces based on the combining variational mode decomposition (VMD) and Gram-Schmidt orthogonalization (GSO). VMD is used to decompose the power traces into sub-signals (modes) and POIs selection process based on GSO is conducted on these sub-signals. As a result, the selected POIs are used for SVM classifier to conduct profiled attack. This attack method outperforms other profiled attacks in the same attack scenario. Experiments were performed on a trace data set collected from the Atmega8515 smart card with AES-128 run on the Sakura-G/W side channel evaluation board and the DPA Contest v4 dataset to verify the effectiveness of our method in reducing number of power traces for the attacks, especially with noisy power traces.
Tóm tắt—Hiện nay, tấn công mẫu được xem là một trong những tấn công kênh kề (SCA) mạnh. Các thuật toán học máy, ví dụ như máy vector hỗ trợ (SVM), thường được sử dụng để nâng cao hiệu quả của tấn công mẫu. Một thách thức đối với tấn công mẫu sử dụng SVM là cần phải tìm được các điểm thích hợp (POI) hay các đặc trưng từ vết điện năng tiêu thụ. Công trình nghiên cứu này đề xuất một phương pháp mới đề tìm POI của vết điện năng tiêu thụ bằng cách kết hợp kỹ thuật phân tích mode biến phân (VMD) và quá trình trực giao hóa Gram-Schmidt (GSO). Trong đó, VMD được sử dụng để phân tách vết điện năng tiêu thụ thành các tín hiệu con còn gọi là VMD mode và việc lựa chọn POIs trên VMD mode này được thực hiện dựa trên quá trình GSO. Dựa trên phương pháp lựa chọn POIs này, chúng tôi đề xuất phương pháp tấn công mẫu sử dụng SVM có hiệu quả tốt hơn các tấn công mẫu khác ở cùng kịch bản tấn công. Các thí nghiệm tấn công được thực hiện trên tập dữ liệu được thu thập từ thẻ thông minh Atmega8515 cài đặt AES-128 chạy trên nền tảng thiết bị tấn công kênh kề Sakura-G/W và tập dữ liệu DPA Contest v4, để chứng minh tính hiệu quả của phương pháp của chúng tôi, trong việc giảm số lượng vết điện năng tiêu thụ cần cho cuộc tấn công, đặc biệt trong trường hợp các điện năng tiêu thụ có nhiễu.
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References
Kocher P., Jaffe J., Jun B. “Differential Power Analysis”. In Proceedings of the 19th Annual International Cryptology Conference on Advances in Cryptology. London (UK), 1999, pp. 388–397.
Brier E., Clavier C., Olivier F. “Correlation Power Analysis with a Leakage Model”. In: Joye M., Quisquater JJ. (eds) Cryptographic Hardware and Embedded Systems - CHES 2004. CHES 2004. Lecture Notes in Computer Science, vol 3156. Springer, Berlin, Heidelberg.
Gierlichs B., Batina L., Tuyls P., Preneel B. “Mutual Information Analysis”. In: Oswald E., Rohatgi P. (eds) Cryptographic Hardware and Embedded Systems – CHES 2008. CHES 2008. Lecture Notes in Computer Science, vol 5154. Springer, Berlin, Heidelberg.
Chari S., Rao J.R., Rohatgi P. “Template Attacks”. In: Kaliski B.S., Koç .K., Paar C. (eds) Cryptographic Hardware and Embedded Systems - CHES 2002. CHES 2002. Lecture Notes in Computer Science, vol 2523. Springer, Berlin, Heidelberg.
Heuser A., Zohner M. “Intelligent Machine Homicide.” In: Schindler W., Huss S.A. (eds) Constructive Side-Channel Analysis and Secure Design. COSADE 2012. Lecture Notes in Computer Science, vol 7275. Springer, Berlin, Heidelberg.
Hospodar, G., Gierlichs, B., De Mulder, E. et al. “Machine learning in side-channel analysis: a first study.” J Cryptogr Eng 1, 293. 2011.
Hospodar, G., De Mulder, E., Gierlichs, B., Vandewalle, J., Verbauwhede, I. “Least Squares Support Vector Machines for Side-Channel Analysis”. In: COSADE 2011. CASED, Darmstadt.
S. Picek et al. “Side-channel analysis and machine learning: A practical perspective”. 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, 2017, pp. 4095-4102.
Zheng, Y., Zhou, Y., Yu, Z., Hu, C., Zhang, H. "How to compare selections of points of interest for side-channel distinguishers in practice?" Information and Communications Security: 16th International Conference, ICICS 2014, Hong Kong, China.
Rechberger C., Oswald E. "Practical Template Attacks." Information Security Applications. WISA 2004.
Gierlichs B., Lemke-Rust K., Paar C. "Templates vs. Stochastic Methods". In Goubin L., Matsui M. (eds) Cryptographic Hardware and Embedded Systems - CHES 2006. Lecture Notes in Computer Science, vol 4249, Springer, Berlin, Heidelberg, 2006, pp. 15-29.
Stefan Mangard, Elisabeth Oswald, and Thomas Popp. “Power Analysis Attacks:Revealing the Secrets of Smart Cards”. Springer US, 2007.
Lomné V., Prouff E., Roche T. "Behind the Scene of Side Channel Attacks". In Sako K., Sarkar P. (eds) Advances in Cryptology - ASIACRYPT 2013. ASIACRYPT 2013. Lecture Notes in Computer Science, vol 8269, Springer, Berlin, Heidelberg, 2013, pp. 506-525.
Lerman, L., Bontempi, G., Markowitch, O. "Side channel attack: an approach based on machine learning". In COSADE 2011 - Second International Workshop on Constructive Side-Channel, 2011.
Liu, J., Zhou, Y., Han, Y., Li, J., Yang, S., Feng, D. "How to characterize side-channel leakages more accurately?". In ISPEC 2011 - Information Security Practice and Experience:7th International Conference, Guangzhou, China, 2011.
Houssem Maghrebi, Thibault Portigliatti, and Emmanuel Prouff. "Breaking cryptographic implementations using deep learning techniques". In Claude Carlet, M. Anwar Hasan, and Vishal Saraswat, editors, Security, Privacy, and Applied Cryptography Engineering, Springer International Publishing. ISBN 978-3-319-49445-6, 2016, pp. 3-26.
Picek, S., Heuser, A., Jovic, A., Legay, A. "On the relevance of feature selection for profiled side-channel attacks". Cryptology ePrint Archive, Report 2017/1110, https://eprint.iacr.org/2017/, 2017.
Bartkewitz, T., Lemke-Rust, K. "Efficient template attacks based on probabilistic multi-class support vector machines". In Mangard, S. (ed.) Smart Card Research and Advanced Applications:11th International Conference, CARDIS 2012, Graz, Austria, 2012.
Dragomiretskiy K and Zosso D. "Variational Mode Decomposition". IEEE Transactions on Signal, vol. 62, pp. 513-544, 2014.
H. Stoppiglia, G. Dreyfus, R. Dubois, Y. Oussar. "Ranking a random feature for variable and feature selection". J. Mach. Learn, vol. 3, pp. 1399-1414, 2003.
Standaert FX., Malkin T.G., Yung M. "A Unified Framework for the Analysis of Side-Channel Key Recovery Attacks". In In: Joux A. (eds) Advances in Cryptology - EUROCRYPT 2009. EUROCRYPT 2009. Lecture Notes in Computer Science, vol 5479, Springer, Berlin, Heidelberg, 2009.
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