Side-channel attacks allow extracting secret information from the execution of cryptographic primitives by correlating the partially known computed data and the measured side-channel signal. However, to set up a successful side-channel attack, the attacker has to perform i) the challenging task of locating the time instant in which the target cryptographic primitive is executed inside a side-channel trace and then ii)the time-alignment of the measured data on that time instant. This paper presents a novel deep-learning technique to locate the time instant in which the target computed cryptographic operations are executed in the side-channel trace. In contrast to state-of-the-art solutions, the proposed methodology works even in the presence of trace deformations obtained through random delay insertion techniques. We validated our proposal through a successful attack against a variety of unprotected and protected cryptographic primitives that have been executed on an FPGA-implemented system-on-chip featuring a RISC-V CPU.

翻译：侧信道攻击通过将部分已知的计算数据与测量的侧信道信号进行关联，能够从密码原语的执行过程中提取秘密信息。然而，要成功实施侧信道攻击，攻击者需要完成以下两项任务：i) 在侧信道轨迹中定位目标密码原语执行的时间点这一具有挑战性的任务；ii) 在对应时间点上对测量数据进行时间对齐。本文提出了一种新颖的深度学习技术，用于在侧信道轨迹中定位目标计算密码运算执行的时间点。与现有解决方案相比，所提出的方法即使面对通过随机延迟插入技术获得的轨迹变形场景也能有效工作。我们通过针对FPGA实现的RISC-V CPU片上系统上执行的多种未保护及受保护密码原语的成功攻击，验证了所提方案的有效性。