The exponential growth in the number of Internet of Things (IoT) devices has seen the introduction of several Lightweight Encryption Algorithms (LEA). While LEAs are designed to enhance the integrity, privacy and security of data collected and transmitted by IoT devices, it is hazardous to assume that all LEAs are secure and exhibit similar levels of protection. To improve encryption strength, cryptanalysts and algorithm designers routinely probe LEAs using various cryptanalysis techniques to identify vulnerabilities and limitations of LEAs. Despite recent improvements in the efficiency of cryptanalysis utilising heuristic methods and a Partial Difference Distribution Table (PDDT), the process remains inefficient, with the random nature of the heuristic inhibiting reproducible results. However, the use of a PDDT presents opportunities to identify relationships between differentials utilising knowledge graphs, leading to the identification of efficient paths throughout the PDDT. This paper introduces the novel use of knowledge graphs to identify intricate relationships between differentials in the SIMON LEA, allowing for the identification of optimal paths throughout the differentials, and increasing the effectiveness of the differential security analyses of SIMON.

翻译：物联网设备数量的指数级增长催生了多种轻量级加密算法。尽管轻量级加密算法旨在增强物联网设备所收集和传输数据的完整性、隐私性与安全性，但假设所有轻量级加密算法均安全且具备同等防护能力则存在风险。为提升加密强度，密码分析人员与算法设计者通常采用多种密码分析技术探测轻量级加密算法的漏洞与局限性。尽管近年来利用启发式方法与部分差分分布表进行的密码分析效率有所提升，但该过程仍显低效——启发式算法的随机性制约了结果的可复现性。然而，部分差分分布表为利用知识图谱识别差分间关联提供了契机，进而可定位贯穿整个部分差分分布表的高效路径。本文首次提出利用知识图谱识别SIMON轻量级加密算法中差分间的复杂关联，实现最优差分路径的定位，并提升SIMON差分安全分析的有效性。