Lightweight Cryptanalysis of IoT Encryption Algorithms : Is Quota Sampling the Answer?

TL;DR

This work addresses the inefficiency of differential cryptanalysis on IoT-friendly lightweight ciphers (SIMON and SIMECK) by introducing VISTA-CRYPT, a quota-sampling–driven enhancement of Nested Monte-Carlo Search (NMCS). By enforcing representative sampling and enabling early termination, the approach reduces variance and dramatically cuts run times, achieving up to $76\%$ time savings in favorable cases and substantial reductions in iteration counts. The authors provide a detailed methodology, experimental code adjustments, and a preliminary graph-based analysis to uncover data relationships, plus a robust set of results showing consistent performance gains over the state-of-the-art. These findings suggest practical, scalable improvements for cryptanalysis workflows on resource-constrained devices and point to future research in applying quota sampling to broader classes of block ciphers and in graph-based cryptanalytic analyses.

Abstract

Rapid growth in the number of small sensor devices known as the Internet of Things (IoT) has seen the development of lightweight encryption algorithms. Two well-known lightweight algorithms are SIMON and SIMECK which have been specifically designed for use on resource-constrained IoT devices. These lightweight encryption algorithms are based on the efficient Feistel block structure which is known to exhibit vulnerabilities to differential cryptanalysis. Consequently, it is necessary to test these algorithms for resilience against such attacks. While existing state-of-the-art research has demonstrated novel heuristic methods of differential cryptanalysis that improve time efficiency on previous techniques, the large state sizes of these encryption algorithms inhibit cryptanalysis time efficiency. In this paper, we introduce Versatile Investigative Sampling Technique for Advanced Cryptanalysis (VISTA-CRYPT) - a time-efficient enhancement of differential cryptanalysis of lightweight encryption algorithms. The proposed technique introduces a simple framework of quota sampling that produces state-of-the-art results with time reductions of up to $76\%$ over existing techniques. Further, we present a preliminary graph-based analysis of the output differentials for the identification of relationships within the data and future research opportunities to further enhance the performance of differential cryptanalysis. The code designed for this work and associated datasets will be available at https://github.com/johncook1979/simon-cryptanalysis.

Figures (14)

• Figure 1: Our methodology: $1$) Extract output differentials: Extract output differentials (C) from lists that also contain left input (A), right input (B) and weight (D). $2$) Define proportional sample size: Define the proportion of differentials to use in the sample. $3$) Distribution extraction: Extract the differentials with a minimum of one of each type to the sample. $4$) Sample generation: A sample based on a quota is generated. $5$) Random path selection: A random path from the sample is chosen. $6$) Decision on search efficiency: Determine if the current search is efficient and terminate early if not. In contrast, the existing state-of-the-art technique selects a random path from the full list of differentials which has a higher degree of variance and is less efficient.
• Figure 2: SIMON and SIMECK round functions
• Figure 3: Transition through Bitwise AND
• Figure 4: Heuristic nested tree search
• Figure 5: Heuristic NMCS with quota sampling