Further observations on the security of Speck32-like ciphers using machine learning.

ABSTRACT: With the widespread deployment of Internet of Things across various industries, the security of communications between different devices is one of the critical concerns to consider. The lightweight cryptography emerges as a specialized solution to address security requirements for resource-constrained environments. Consequently, the comprehensive security evaluation of the lightweight cryptographic primitives-from the structure of ciphers and cryptographic components-has become imperative. In this article, we focus on the security evaluation of rotation parameters in the Speck32-like lightweight cipher family. We establish a machine learning-driven security evaluation framework for the rotational parameter selection principles-the core of Speck32's design architecture. To assess different

SUBMITTER: Hou Z 

PROVIDER: S-EPMC12453772 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature