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软件学报 ISSN 1000-9825, CODEN RUXUEW E-mail: jos@iscas.ac.cn
2026,37(1):442−463 [doi: 10.13328/j.cnki.jos.007454] [CSTR: 32375.14.jos.007454] http://www.jos.org.cn
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复杂应用场景下侧信道分析的可移植性研究综述
李 迪, 张裕鹏, 汤宇锋, 龚 征
(华南师范大学 计算机学院, 广东 广州 510631)
通信作者: 龚征, E-mail: gongzheng@scnu.edu.cn
摘 要: 侧信道分析 (side-channel analysis, SCA) 是一种通过获取软硬件运行时产生的泄露信息来破解密钥的分析
技术. 其中, 建模类侧信道分析已被证明是攻击密码系统的一种强有力的手段. 近年来, 随着人工智能技术的发展,
其在建模类侧信道分析中的应用极大丰富了攻击手段, 并显著提升了攻击效率. 在该类方法的建模阶段, 攻击者通
过访问克隆设备以收集与目标设备相关的泄露信息, 但在实际场景中, 克隆设备与目标设备之间往往存在差异. 然
而, 大部分的研究工作仅考虑使用一种设备进行支持和验证, 这导致所建立的方法依赖于特定环境, 其应用范围有
限, 可移植性差. 为了解决该问题, 重点聚焦于复杂应用场景下面临的攻击可移植性问题, 深入探讨在不同参数设
置、算法实现、设备差异等多方面所引发的挑战, 并对近年来国际上学者提出的解决方案和分析结果进行系统梳
理. 在此基础上, 进一步总结当前侧信道分析可移植性研究中存在的不足, 并展望未来的发展方向.
关键词: 侧信道分析 (SCA); 可移植性; 迁移学习; 深度学习; 物理安全
中图法分类号: TP309
中文引用格式: 李迪, 张裕鹏, 汤宇锋, 龚征. 复杂应用场景下侧信道分析的可移植性研究综述. 软件学报, 2026, 37(1): 442–463. http://
www.jos.org.cn/1000-9825/7454.htm
英文引用格式: Li D, Zhang YP, Tang YF, Gong Z. Review of Portability Research on Side-channel Analysis in Complex Application
Scenarios. Ruan Jian Xue Bao/Journal of Software, 2026, 37(1): 442–463 (in Chinese). http://www.jos.org.cn/1000-9825/7454.htm
Review of Portability Research on Side-channel Analysis in Complex Application Scenarios
LI Di, ZHANG Yu-Peng, TANG Yu-Feng, GONG Zheng
(School of Computer Science, South China Normal University, Guangzhou 510631, China)
Abstract: Side-channel analysis (SCA) is a technique that extracts leaked information generated during hardware or software execution to
compromise cryptographic keys. Among various approaches, profiling side-channel analysis has been proven to be a powerful method for
attacking cryptographic systems. In recent years, the integration of artificial intelligence technology into profiling side-channel analysis has
significantly enriched attack strategies and improved efficiency. During the profiling phase, leakage information related to the target device
is typically collected by accessing a cloned device. However, practical scenarios often involve discrepancies between the cloned and target
devices. Most existing studies rely on a single device for training and validation, resulting in methods that are highly environment-
dependent, with limited applicability and poor portability. This study focuses on the portability challenges encountered in complex
application scenarios. Challenges arising from variations in parameter settings, algorithm implementations, and hardware differences are
analyzed in detail. Solutions and analysis results proposed in recent years are systematically reviewed. Based on this survey, current
limitations in portability research on side-channel analysis are summarized, and potential future directions are discussed.
Key words: side-channel analysis (SCA); portability; transfer learning; deep learning; physical security
1 引 言
基于高性能服务器或轻量级微处理器, 各种信息系统与应用在互联网时代得到了飞速的发展. 与此同时, 人们
* 基金项目: 国家自然科学基金 (U2336209); 广东省科技计划 (2022A1515140090)
收稿时间: 2024-03-18; 修改时间: 2024-11-19; 采用时间: 2025-04-19; jos 在线出版时间: 2025-09-10
CNKI 网络首发时间: 2025-09-11
