Side channel attack detection based on improved support vector machine
Subject Areas : networkDaniel Jafari 1 , علیرضا موحدیان 2
1 -
2 - گروه مهندسی برق، دانشکده مهندسی، دانشگاه بین المللی امام رضا (ع)
Keywords: Cheetah Algorithm, Support Vector Machine, Detection of Side Channel Attacks,
Abstract :
In the articles, various machine learning techniques have been used in the fields of detecting side channel attacks. One of the suitable methods for this purpose is support vector machine. In the latest side channel attack detection research, the metal annealing method is used to simulate and improve the support vector machine. Accurate setting of support vector machine parameters is of great importance to detect side channel attacks, which is considered part of NP-hard problems, and using meta-heuristic methods is a suitable solution for these problems. Search algorithms with high convergence power can reach suitable values for parameters and thus increase the accuracy of attack detection. Cheetah meta-heuristic algorithm has higher convergence power than optimization methods including annealing method. In this article, the cheetah method is used for the first time to improve the support vector machine method in detecting side channel attacks. The simulation results have been used from the DPA Contest v4 dataset, which shows that the proposed method has been able to improve the results of the detection accuracy compared to the support vector machine method and its improved version, the simulated annealing algorithm, by 1%.
[1] Kocher, P.C. Timing Attacks on Implementations of Diffie-Hellman, RSA, DSS, and Other Systems. In Proceedings of the 16th Annual International Cryptology Conference (CRYPTO 96), Santa Barbara, CA, USA, 18–22 August 1996; pp. ۱۰۴–113.
[2] Wang, R.; Wang, H.; Dubrova, E. Far Field EM Side-Channel Attack on AES Using Deep Learning. In Proceedings of the 4th ACM Workshop on Attacks and Solutions in Hardware Security, online, 13 November 2020; pp. ۳۵–44.
[3] Ferrigno, J.; Hlaváˇc, M. When AES Blinks: Introducing Optical Side Channel. IET Inf. Secur. ۲۰۰۸, 2, 94.
[4] Genkin, D.; Shamir, A.; Tromer, E. Acoustic Cryptanalysis. J. Cryptol. ۲۰۱۷, 30, 392–443.
[5] Kocher, P.C.: Timing attacks on implementations of DiffieHellman, RSA, DSS, and other systems. Advances in Cryptology– CRYPTO ’96: 16th Annual International Cryptology Conference Santa Barbara. California, USA August 18–22, 1996 Proceedings, pp. 104–113. Springer, Berlin (1996)
[6] Kocher, P., Jaffe, J., Jun, B.: Differential power analysis. Advances in Cryptology–CRYPTO’ 99: 19th Annual International Cryptology Conference Santa Barbara, California, USA, August 15–19, 1999. Proceedings, pp. 388–397. Springer, Berlin (1999)
[7] Quisquater, J.J., Samyde, D.: Electromagnetic analysis (EMA): measures and countermeasures for smart cards. Smart Card Programming and Security: International Conference on Research in Smart Cards, E-smart 2001 Cannes, France, September 19–21, 2001. Proceedings, pp. 200–210. Springer, Berlin (2001)
[8] Genkin, D., Shamir, A., Tromer, E.: Acoustic cryptanalysis. J. Cryptol. ۳۰(2), 392–443 (2017)
[9] Zhuang, L., Zhou, F., Tygar, J.D.: Keyboard acoustic emanations revisited. ACM Trans. Inf. Syst. Secur. ۱۳(1), 3:1–3:26 (2009)
[10] Eisenbarth, T., Paar, C., Weghenkel, B.: Building a side channel-based disassembler. Transactions on Computational Science X: Special Issue on Security in Computing, Part I, pp. 78–99. Springer, Berlin (2010)
[11] chindler, W., Lemke, K., Paar, C.: A stochastic model for differential side channel cryptanalysis. In: Rao, J.R., Sunar, B. (eds.) Cryptographic Hardware and Embedded Systems—CHES 2005: 7th International Workshop, Edinburgh, UK, August 29– September 1, 2005. Proceedings, pp. 30–46. Springer, Berlin (2005)
[12] Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference and Prediction, 2nd edn. Springer, Berlin (2009)
[13] Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–26
[14] Jap, D., Breier, J.: Overview of machine learning based sidechannel analysis methods. In: 2014 International Symposium on Integrated Circuits (ISIC), pp. ۳۸–41 (2014)
[15] Ying Zhang, Pengfei He , Han Gan , Hongxin Zhang ,Pengfei Fan: Side-Channel Power Analysis Based on SA-SVM.in:2023 applted sciences 3:1–3:26 (2023)
[16] Mohammad Amin Akbari, Mohsen Zare, Rasoul Azizipanah‑abarghooee,Seyedali Mirjalil & Mohamed Deriche1 . (2022). The cheetah optimizer:a nature.inspired metaheuristic algorithm for large.scale optimization problems . Scientific Reports.10953.
[17] Hospodar, G., Gierlichs, B., De Mulder, E., Verbauwhede, I., Vandewalle, J.: Machine learning in side-channel analysis: a first study. J. Cryptogr. Eng. ۱(4), 293 (2011)
[18] Heuser, A., Zohner, M.: Intelligent machine homicide. In: Schindler, W., Huss, S.A. (eds.) Constructive Side-Channel Analysis and Secure Design: Third International Workshop, COSADE 2012, Darmstadt, Germany, May 3–4, 2012. Proceedings. Springer, Berlin (2012)
[19] 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, November 28–30, 2012, Revised Selected Papers, pp. 263–276. Springer, Berlin (2013)
[20] Standaert, F.X., Malkin, T.G., Yung, M.: A unified framework for the analysis of side-channel key recovery attacks. In: Joux, A. (ed.) Advances in Cryptology—EUROCRYPT 2009: 28th Annual International Conference on the Theory and Applications of Cryptographic Techniques, Cologne, Germany, April 26–30, 2009. Proceedings. Springer, Berlin (2009)
[21] Mangard, S.: A simple power-analysis (SPA) attack on implementations of the AES key expansion. In: Lee, P.J., Lim, C.H. (eds.) Information Security and Cryptology—ICISC 2002: 5th International Conference Seoul, Korea, November 28–29, 2002. Revised Papers, pp. 343–358. Springer, Berlin (2003)
[22] Renauld, M., Standaert, F.X.: Algebraic side-channel attacks. In: Bao, F., Yung, M., Lin, D., Jing, J. (eds.) Information Security and Cryptology: 5th International Conference, Inscrypt 2009, Beijing, China, December 12–15, 2009. Revised Selected Papers, pp. 393– 410. Springer, Berlin (2010)
[23] Picek, S., Heuser, A., Jovic, A., Ludwig, S.A., Guilley, S., Jakobovic, D., Mentens, N.: Side-channel analysis and machine learning: A practical perspective. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. ۴۰۹۵–4102 (2017)
[24] Rob Oshana.: A Side Channel Attack Detection System Using Processor Core Events and a Support Vector Machine. In:2022 Mediterranean Conference on Embedded Computing (MECO). (2022)
[25] MATTEO NERINI.: Machine Learning for PIN Side-Channel Attacks Based on Smartphone Motion Sensors. In: IEEE Access. (2023)
[26] ALEJANDRO DOMÍNGUEZ CAMPOS.: Intrusion detection on IoT environments through side-channel and Machine Learning techniques. In: IEEE Access. (2024)
[27] Alejandro Almeida, Muneeba Asif.: Side-Channel-Driven Intrusion Detection System for Mission Critical Unmanned Aerial Vehicles. In: IEEE Access. (2023)
[28] PRIYADHARSHINI MOHANRAJ : A Multiobjective Approach for Side-Channel Based Hardware Trojan Detection Using Power Traces In IEICE TRANS. (2024)
[29] Goos, G.; Hartmanis, J.; van Leeuwen, J.; Kocher, P.; Jaffe, J.; Jun, B. Differential Power Analysis. In Proceedings of the 19th Annual International Cryptology Conference (CRYPTO 99), Santa Barbara, CA, USA, 15–19 August 1999; pp. ۳۸۸–397.
[30] Gierlichs, B.; Batina, L.; Tuyls, P.; Preneel, B. Mutual Information Analysis. In Cryptographic Hardware and Embedded Systems— CHES 2008; Oswald, E., Rohatgi, P., Eds.; Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2008; Volume 5154, pp. 426–442. ISBN 978-3-540-85052-6.
[31] Niu, Y.; Zhang, J.; Wang, A.; Chen, C. An Efficient Collision Power Attack on AES Encryption in Edge Computing. IEEE Access 2019, 7, 18734–18748.
[32] Han, J.; Kim, Y.-J.; Kim, S.-J.; Sim, B.-Y.; Han, D.-G. Improved Correlation Power Analysis on Bitslice Block Ciphers. IEEE Access 2022, 10, 39387–39396.
[33] Choudary, M.O.; Kuhn, M.G. Efficient, Portable Template Attacks. IEEE Trans. Inf. Forensic Secur. ۲۰۱۸, 13, 490–501
[34] Golder, A.; Das, D.; Danial, J.; Ghosh, S.; Sen, S.; Raychowdhury, A. Practical Approaches Toward Deep-Learning-Based Cross-Device Power Side-Channel Attack. IEEE Trans. VLSI Syst. ۲۰۱۹, 27, 2720–2733.
[35] Picek, S.; Heuser, A.; Jovic, A.; Legay, A. Climbing Down the Hierarchy: Hierarchical Classification for Machine Learning Side-Channel Attacks. In Proceedings of the 9th International Conference on Cryptology in Africa (AFRICACRYPT 2017), Dakar, Senegal, 24–26 May 2017; pp. ۶۱–78.
[36] Liu, J.; Zhang, S.; Luo, Y.; Cao, L. Machine Learning-Based Similarity Attacks for Chaos-Based Cryptosystems. IEEE Trans. Emerg. Top. Comput. ۲۰۲۱, 10, 824–837
[37] Martinasek, Z.; Hajny, J.; Malina, L. Optimization of Power Analysis Using Neural Network. In Proceedings of the 10th IFIP WG 8.8/11.2 International Conference (CARDIS 2011), Leuven, Belgium, 14–16 September 2011; pp. ۹۴–107.
[38] Hospodar, G.; Gierlichs, B.; De Mulder, E.; Verbauwhede, I.; Vandewalle, J. Machine Learning in Side-Channel Analysis: A First Study. J. Cryptogr. Eng. ۲۰۱۱, 1, 293–302.
[39] Heuser, A.; Zohner, M. Intelligent Machine Homicide. In Proceedings of the 10th International Workshop, COSADE 2019, Darmstadt, Germany, 3–5 April 2019; pp. ۲۴۹–264.
[40] Hou, S.; Zhou, Y.; Liu, H.; Zhu, N. Wavelet Support Vector Machine Algorithm in Power Analysis Attacks. Radioengineering 2017, 26, 890–902.
[41] Picek, S.; Heuser, A.; Jovic, A.; Bhasin, S.; Regazzoni, F. The Curse of Class Imbalance and Conflicting Metrics with Machine Learning for Side-Channel Evaluations. IACR Trans. Cryptogr. Hardw. Embed. Syst. ۲۰۱۸, 2019, 209–237
[42] Tran, N.Q.; Hur, J.; Nguyen, H.M. Effective Feature Extraction Method for SVM-Based Profiled Attacks. Comput. Inf. ۲۰۲۱, 40, 1108–1135.
[43] Martinasek, Z., Zeman, V., Malina, L., Martinasek, J.: k-Nearest neighbors algorithm in profiling power analysis attacks. Radioengineering 25(2), 365–382 (2016)
[44] Lerman, L., Bontempi, G., Markowitch, O.: A machine learning approach against a masked AES. J. Cryptogr. Eng. ۵(2), 123–139 (2015)
[45] Duan, L., Hongxin, Z., Qiang, L., Xinjie, Z., Pengfei, H.: Electromagnetic side-channel attack based on PSO directed acyclic graph SVM. J. China Univ. Posts Telecommun. ۲۲(5), 10–15 (2015)
[46] Maghrebi, H., Portigliatti, T., Prouff, E.: Breaking cryptographic implementations using deep learning techniques. In: Carlet, C., Hasan, M.A., Saraswat, V. (eds.) Security, Privacy, and Applied Cryptography Engineering: 6th International Conference, SPACE 2016, Hyderabad, India, December 14–18, 2016. Proceedings, pp. 3–26. Springer, Cham (2016)
[47] Wang, A.; Li, Y.; Ding, Y.; Zhu, L.; Wang, Y. Efficient Framework for Genetic Algorithm-Based Correlation Power Analysis. IEEE Trans. Inf. Forensics Secur. ۲۰۲۱, 16, 4882–4894.
[48] Wang, C.X.; Zhao, S.Y.; Wang, X.S.; Luo, M.; Yang, M. A Neural Network Trojan Detection Method Based on Particle Swarm Optimization. In Proceedings of the 14th International Conference on Solid-State and Integrated Circuit Technology (ICSICT), Qingdao, China, 31 October–3 November 2018; pp. ۱–3.
[49] Huang, C.-L.; Wang, C.-J. A GA-Based Feature Selection and Parameters Optimizationfor Support Vector Machines. Expert Syst. Appl. ۲۰۰۶, 31, 231–240.
[50] Lin, S.-W.; Ying, K.-C.; Chen, S.-C.; Lee, Z.-J. Particle Swarm Optimization for Parameter Determination and Feature Selection of Support Vector Machines. Expert Syst. Appl. ۲۰۰۸, 35, 1817–1824.
[51] Zhang, X.; Chen, X.; He, Z. An ACO-Based Algorithm for Parameter Optimization of Support Vector Machines. Expert Syst. Appl. ۲۰۱۰, 37, 6618–6628.
[52] Sartakhti, J.S.; Afrabandpey, H.; Saraee, M. Simulated Annealing Least Squares Twin Support Vector Machine (SA-LSTSVM) for Pattern Classification. Soft Comput. ۲۰۱۷, 21, 4361–4373.
[53] Yin, Z.; Zheng, J.; Huang, L.; Gao, Y.; Peng, H.; Yin, L. SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton Robot. Appl. Sci. ۲۰۲۱, 11, 5573.
[54] DPA Contest V4. Available online: https://www.dpacontest.org/v4/rsm_doc.php (accessed on 20 March 2023).
