使用Neo4j对SIMON密码进行密码分析
Cryptanalysis of the SIMON Cypher Using Neo4j
AI总结：
针对轻量加密算法密码分析效率低、结果难复现的问题，本文创新性地用知识图谱分析SIMON密码的差分关联，寻找PDDT中的最优路径，提升差分安全分析效果。
摘要：
随着物联网（IoT）设备数量呈指数级增长，多种轻量加密算法（Lightweight Encryption Algorithms，LEA）被相继推出。尽管LEA的设计初衷是提升IoT设备采集与传输数据的完整性、隐私性与安全性，但贸然认定所有LEA都安全且防护水平相当是危险的。为增强加密强度，密码分析人员与算法设计者会常规性地运用各类密码分析技术检测LEA，以识别其漏洞与局限。尽管近年来借助启发式方法与部分差分分布表（Partial Difference Distribution Table，PDDT）提升了密码分析的效率，但该过程效率仍然偏低，且启发式方法的随机性导致结果难以复现。不过，PDDT的应用为利用知识图谱识别差分之间的关联提供了可能，进而可在整个PDDT中找到高效路径。本文创新性地将知识图谱用于识别SIMON类LEA中差分之间的复杂关联，能够在各差分之间找到最优路径，提升SIMON差分安全分析的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
