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

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 parameters security, we develop neural-differential distinguishers with considering of two distinct input difference models: (1) the low-Hamming-weight input differences and (2) the input differences from optimal differential characteristics. Our methodology achieves the security evaluation of 256 rotation parameters using the accuracy of neural distinguishers as the evaluation criteria. Our results illustrate the parameter (7,3) has stronger ability to resist machine learning-aided distinguishing attack compared to the standard (7,2) configuration. To our knowledge, this represents the first comprehensive study applying machine learning techniques for security assessment of Speck32-like ciphers. Furthermore, we investigate the reason for the difference in the accuracy of neural distinguishers with different rotation parameters. Our experimental results demonstrate that the bit bias in output differences and truncated differences is the important factor affecting the accuracy of distinguishers.