Impact of Differentials in SIMON32 Algorithm for Lightweight Security of Internet of Things

TL;DR

This paper analyzes the differential properties of the SIMON32 cipher to improve cryptanalysis efficiency, identifying high probability differentials and extending the number of targeted rounds beyond current benchmarks.

Contribution

It introduces a novel differential analysis of SIMON32, revealing properties that enhance cryptanalysis and surpass existing state-of-the-art results.

Findings

Increased number of targeted rounds in cryptanalysis.

Identification of high probability differentials.

Enhanced efficiency in cryptanalysis techniques.

Abstract

SIMON and SPECK were among the first efficient encryption algorithms introduced for resource-constrained applications. SIMON is suitable for Internet of Things (IoT) devices and has rapidly attracted the attention of the research community to understand its structure and analyse its security. To analyse the security of an encryption algorithm, researchers often employ cryptanalysis techniques. However, cryptanalysis is a resource and time-intensive task. To improve cryptanalysis efficiency, state-of-the-art research has proposed implementing heuristic search and sampling methods. Despite recent advances, the cryptanalysis of the SIMON cypher remains inefficient. Contributing factors are the large size of the difference distribution tables utilised in cryptanalysis and the scarcity of differentials with a high transition probability. To address these limitations, we introduce an analysis of differential properties of the SIMON32 cypher, revealing differential characteristics that pave the way for future efficiency enhancements. Our analysis has further increased the number of targeted rounds by identifying high probability differentials within a partial difference distribution table of the SIMON cypher, exceeding existing state-of-the-art benchmarks. The code designed for this work is available at https://github.com/johncook1979/simon32-analysis.