Titlebook: Constructive Side-Channel Analysis and Secure Design; 13th International W Josep Balasch,Colin O’Flynn Conference proceedings 2022 The Edit

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Cytology

眉毛

https://doi.org/10.1007/978-3-642-23611-2esides commonly used metrics like guessing entropy, one should also show the standard deviation results to assess the attack performance properly. Even more importantly, using the arithmetic mean for guessing entropy does not yield the best results, and instead, a median value should be used.

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German Taxation of Real Estate Investmentssis of the . dataset should be revisited and that the more recent . dataset with variable key training should be used in future work. Finally, we investigate the attack success for all bytes. Performance differences with the same network architecture for different bytes highlight that even traces of

肉身

Guido Spars,Inès-Caroline Naismithware changes, which are unrealistic for existing architectures. Thus, a wide range of systems remains unprotected and vulnerable to control-flow attacks..This work presents FIPAC, a software-based CFI scheme protecting the execution at basic block granularity against software . fault attacks. FIPAC

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爭吵加

https://doi.org/10.1007/978-3-642-25029-3 and question the usefulness of the reported performance numbers in the face of the implementations’ poor side-channel resistance. This work serves as a reminder that practical evaluations cannot be omitted in the context of side-channel analysis.

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NOTCH

REP

Machine-Learning Assisted Side-Channel Attacks on RNS ECC Implementations Using Hybrid Feature Enginof raw features and advanced hybrid feature engineering techniques. We discuss the metrics and procedures that can be used for accurate classification on the imbalanced datasets. The experimental results demonstrate that, for ECC RNS datasets, the efficiency of simple machine learning algorithms is