Hound：基于深度学习的失同步侧信道轨迹密码原语定位方法
Hound: Locating Cryptographic Primitives in Desynchronized Side-Channel Traces Using Deep-Learning
AI总结：
针对动态频率缩放导致轨迹变形、阻碍侧信道攻击预处理的问题，提出基于深度学习的Hound流水线，可在失同步侧信道轨迹中定位密码原语，经FPGA片上系统实验验证有效。
摘要：
侧信道攻击通过将部分已知的计算数据与测得的侧信道信号相关联，从密码原语中提取敏感信息。从原始侧信道轨迹出发，对轨迹进行预处理以定位每个密码原语的执行时间，再将所有采集数据重新对齐到该特定时间，是成功实施侧信道攻击的关键步骤。隐藏技术作为一种低成本方案被广泛采用，用于阻碍侧信道轨迹的预处理，从而在实际场景中限制侧信道攻击。本研究提出Hound，一种基于深度学习的新型流水线，即使存在动态频率缩放执行器引入的轨迹变形，也能在侧信道轨迹中定位密码原语的执行过程。研究通过对搭载RISC-V CPU的FPGA片上系统中运行的多种密码原语发起成功攻击，验证了Hound的有效性，实验期间动态频率缩放处于启用状态。实验结果表明，在经动态频率缩放（DFS）变形的侧信道轨迹中识别密码原语是可行的。
英文摘要：
Side-channel attacks allow to extract sensitive information from cryptographic primitives by correlating the partially known computed data and the measured side-channel signal. Starting from the raw side-channel trace, the preprocessing of the side-channel trace to pinpoint the time at which each cryptographic primitive is executed, and, then, to re-align all the collected data to this specific time represent a critical step to setup a successful side-channel attack. The use of hiding techniques has been widely adopted as a low-cost solution to hinder the preprocessing of side-channel traces thus limiting side-channel attacks in real scenarios. This work introduces Hound, a novel deep learning-based pipeline to locate the execution of cryptographic primitives within the side-channel trace even in the presence of trace deformations introduced by the use of dynamic frequency scaling actuators. Hound has been validated through successful attacks on various cryptographic primitives executed on an FPGA-based system-on-chip incorporating a RISC-V CPU, while dynamic frequency scaling is active. Experimental results demonstrate the possibility of identifying the cryptographic primitives in DFS-deformed side-channel traces.
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