Cluster-based side-channel analysis (SCA) is a commonly used side-channel analysis method for public key cryptography systems. This paper focuses on assessment metrics to improve the efficiency and accuracy of key recovery in cluster-based SCA processes. We introduce a novel metric called adjacent distance coefficient (AD coefficient). Different from traditional metrics like silhouette coefficient, membership degree, and information entropy, the AD coefficient, by considering local density and avoiding the computation of cluster centers, is less influenced by the shape and size of data, thereby overcoming limitations of traditional metrics. Experiments on RSA, SM2, and ECC demonstrate that the AD coefficient exhibits higher accuracy compared to traditional metrics, especially under conditions with noise and random delays, where it shows significant robustness. Incorporating the AD coefficient, we propose an assessment method to detect whether cryptographic devices are resistant to cluster-based SCA attacks, offering a convenient quantitative measure for the security evaluation of cryptographic devices.

Fuzzy reasoning is intensively used in intelligent systems including fuzzy control, classification, expert systems, and networks to name a few dominant categories of such architectures. As being a fundamental construct permeating so many diverse areas, fuzzy reasoning was studied with respect to its fundamental properties such as robustness. The notion of robustness or sensitivity becomes of paramount importance by leading to a more comprehensive understanding of the way in which reasoning processes are developed. In this study, we introduce and study properties of some measures of robustness (or sensitivity) of fuzzy connectives and implication operators and discuss their relationships with perturbation properties of fuzzy sets. The results produced here are compared and contrasted with the previous findings available in the literature. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 393–413, 2005.