2023/01/25 by Fiske Schijlen, Schijlen, Fiske, Lichao Wu +3
Computer Science · Engineering · #Cryptographic Implementations and Security #Cryptography and Security (cs.CR) #Digital Media Forensic Detection #Electrostatic Discharge in Electronics #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE)
paper · pdf · doi:10.48550/arxiv.2301.10802
openalex publication_date 2023/01/25 · openalex created_date 2023/01/28 · openalex updated_date 2026/08/01
Side-channel analysis (SCA) can obtain information related to the secret key by exploiting leakages produced by the device. Researchers recently found that neural networks (NNs) can execute a powerful profiling SCA, even on targets protected with countermeasures. This paper explores the effectiveness of Neuroevolution to Attack Side-channel Traces Yielding Convolutional Neural Networks (NASCTY-CNNs), a novel genetic algorithm approach that applies genetic operators on architectures' hyperparameters to produce CNNs for side-channel analysis automatically. The results indicate that we can achieve performance close to state-of-the-art approaches on desynchronized leakages with mask protection, demonstrating that similar neuroevolution methods provide a solid venue for further research. Finally, the commonalities among the constructed NNs provide information on how NASCTY builds effective architectures and deals with the applied countermeasures.