A Deep-Learning Technique to Locate Cryptographic Operations in Side-Channel Traces
This addresses the challenge for attackers in side-channel analysis by automating a critical step, though it is incremental as it builds on existing deep-learning methods for side-channel attacks.
The paper tackles the problem of locating cryptographic operations in side-channel traces, a key step for side-channel attacks, by introducing a deep-learning technique that works even with trace deformations from random delay insertion, achieving successful attacks on various unprotected and protected cryptographic primitives on an FPGA-implemented RISC-V system.
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.