Page 464 - 《软件学报》2026年第1期
P. 464
李迪 等: 复杂应用场景下侧信道分析的可移植性研究综述 461
Springer, 2024. 99–131. [doi: 10.1007/978-981-96-0944-4_4]
[26] Schoos K, Meschkov S, Tahoori MB, Gnad DRE. JitSCA: Jitter-based side-channel analysis in picoscale resolution. IACR Trans. on
Cryptographic Hardware and Embedded Systems, 2023, 3: 294–320. [doi: 10.46586/tches.v2023.i3.294-320]
[27] Perin G, Wu L, Picek S. Exploring feature selection scenarios for deep learning-based side-channel analysis. IACR Trans. on
Cryptographic Hardware and Embedded Systems, 2022, 4: 828–861. [doi: 10.46586/tches.v2022.i4.828-861]
[28] Cagli E, Dumas C, Prouff E. Convolutional neural networks with data augmentation against jitter-based countermeasures: Profiling
attacks without pre-processing. In: Fischer W, Homma N, eds. Proc. of the 19th Int’l Conf. on Cryptographic Hardware and Embedded
Systems. Taipei: Springer, 2017. 45–68. [doi: 10.1007/978-3-319-66787-4_3]
[29] Cao P, Zhang C, Lu XJ, Gu DW, Xu S. Improving deep learning based second-order side-channel analysis with bilinear CNN. IEEE
Trans. on Information Forensics and Security, 2022, 17: 3863–3876. [doi: 10.1109/tifs.2022.3216959]
[30] Masure L, Dumas C, Prouff E. A comprehensive study of deep learning for side-channel analysis. IACR Trans. on Cryptographic
Hardware and Embedded Systems, 2020, 1: 348–375. [doi: 10.13154/tches.v2020.i1.348-375]
[31] Renauld M, Standaert FX, Veyrat-Charvillon N, Kamel D, Flandre D. A formal study of power variability issues and side-channel attacks
for nanoscale devices. In: Proc. of the Annual Int’l Conf. on the 2011 Theory and Applications of Cryptographic Techniques. Tallinn:
Springer, 2011. 109–128. [doi: 10.1007/978-3-642-20465-4_8]
[32] Elaabid MA, Guilley S. Portability of templates. Journal of Cryptographic Engineering, 2012, 2(1): 63–74. [doi: 10.1007/s13389-012-
0030-6]
[33] Lomné V, Prouff E, Roche T. Behind the scene of side channel attacks. In: Sako K, Sarkar P, eds. Proc. of the 19th Int’l Conf. on the
Theory and Application of Cryptology and Information. Bengaluru: Springer, 2013. 506–525. [doi: 10.1007/978-3-642-42033-7_26]
[34] Hanley N, O'Neill M, Tunstall M, Marnane WP. Empirical evaluation of multi-device profiling side-channel attacks. In: Proc. of the 2014
IEEE Workshop on Signal Processing Systems (SiPS). Belfast: IEEE, 2014. 1–6. [doi: 10.1109/SiPS.2014.6986091]
[35] Kim K, Kim TH, Kim T, Ryu S. Black Hat Asia—AES wireless keyboard: Template attack for eavesdropping. 2018. https://i.blackhat.
com/briefings/asia/2018/asia-18-Kim-AES-Wireless-Keyboard-Template-Attack-for-Eavesdropping.pdf
[36] Wang W, Zhang MH, Chen G, Jagadish HV, Ooi BC, Tan KL. Database meets deep learning: Challenges and Opportunities. ACM
SIGMOD Record, 2016, 45(2): 17–22. [doi: 10.1145/3003665.3003669]
[37] van der Valk D, Picek S, Bhasin S. Kilroy was here: The first step towards explainability of neural networks in profiled side-channel
analysis. In: Bertoni GM, Regazzoni F, eds. Proc. of the 11th Int’l Workshop on Constructive Side-channel Analysis and Secure Design.
Lugano: Springer, 2021. 175–199. [doi: 10.1007/978-3-030-68773-1_9]
[38] Das D, Golder A, Danial J, Ghosh S, Raychowdhury A, Sen S. X-DeepSCA: Cross-device deep learning side channel attack. In: Proc. of
the 56th Annual Design Automation Conf. Las Vegas: ACM, 2019. 134. [doi: 10.1145/3316781.3317934]
[39] Carbone M, Conin V, Cornélie MA, Dassance F, Dufresne G, Dumas C, Prouff E, Venelli A. Deep learning to evaluate secure RSA imple-
mentations. IACR Trans. on Cryptographic Hardware and Embedded Systems, 2019, 2: 132–161. [doi: 10.13154/tches.v2019.i2.132-161]
[40] Wouters L, Van den Herrewegen J, Garcia FD, Oswald D, Gierlichs B, Preneel B. Dismantling DST80-based immobiliser systems. IACR
Trans. on Cryptographic Hardware and Embedded Systems, 2020, 2: 99–127. [doi: 10.13154/tches.v2020.i2.99-127]
[41] Wang A, Ge J, Zhang N, Zhang F, Zhang GS. Practical cases of side-channel analysis. Journal of Cryptologic Research, 2018, 5(4):
383–398 (in Chinese with English abstract). [doi: 10.13868/j.cnki.jcr.000249]
[42] Wu WB, Liu Z, Yang H, Zhang JP. Survey of side-channel attacks and countermeasures on post-quantum cryptography. Ruan Jian Xue
Bao/Journal of Software, 2021, 32(4): 1165–1185 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6165.htm [doi: 10.
13328/j.cnki.jos.006165]
[43] Wang YJ, Fan HP, Dai ZY, Yuan QJ, Wang XB. Advances in side channel attacks and countermeasures. Chinese Journal of Computers,
2023, 46(1): 202–228 (in Chinese with English abstract). [doi: 10.11897/SP.J.1016.2023.00202]
[44] Picek S, Perin G, Mariot L, Wu LC, Batina L. SoK: Deep learning-based physical side-channel analysis. ACM Computing Surveys, 2023,
55(11): 227. [doi: 10.1145/3569577]
[45] Bhasin S, Chattopadhyay A, Heuser A, Jap D, Picek S, Shrivastwa RR. Mind the portability: A warriors guide through realistic profiled
side-channel analysis. In: Proc. of the 2020 Network and Distributed System Security (NDSS) Symp. San Diego, 2020. 1–14. [doi: 10.
14722/ndss.2020.24390]
[46] Zhang F, Shao B, Xu GR, Yang BL, Yang ZQ, Qin Z, Ren K. From homogeneous to heterogeneous: Leveraging deep learning based
power analysis across devices. In: Proc. of the 57th ACM/EDAC/IEEE Design Automation Conf. San Francisco: IEEE, 2020. 1–6. [doi:
10.1109/DAC18072.2020.9218693]
[47] Cao P, Zhang C, Lu XJ, Gu DW. Cross-device profiled side-channel attack with unsupervised domain adaptation. IACR Trans. on

