轻量级IoT密码的图表示学习
Graph Representation Learning of Lightweight IoT Ciphers
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中文总结 AI 辅助
针对SIMON、SIMECK等轻量级IoT密码的差分聚类识别空白，采用ML引导的GRL方法，提取pDDT特征构建有向图，KNN模型表现最优，框架可推广至其他相关LCAs家族。
中文摘要 AI 辅助
SIMON和SIMECK属于基于Feistel分组密码的轻量级密码算法（LCAs）家族，专为物联网（IoT）设备设计。与所有Feistel密码一样，它们易受差分密码分析攻击，因此需要严格的抗性评估。尽管最先进的技术利用启发式方法和采样来提高效率，但很少有研究将机器学习（ML）引导的图表示学习（GRL）应用于高效识别和可视化高概率差分簇。我们通过引入一种高效的特征工程策略来解决这一空白，该策略从部分差分分布表（pDDT）中提取四个差分属性，揭示原始差分数据中隐藏的结构信息。利用丰富的特征，我们为SIMON$32$和SIMECK$32$构建并比较了三种ML引导的有向图，分别使用K近邻（KNN）、决策树（DT）和随机森林（RF）。据我们所知，我们的框架生成了第一个基于图的差分聚类效应可视化，其中高概率单比特差分在学习到的嵌入中形成几何上接近的簇。所有三种模型在识别高概率差分方面均达到1.0的精度，确认零误报。KNN实现了最强的簇分离、最高的F1分数和最低的图构建时间，约为2.3秒，而DT和RF则产生具有近乎完美回归的最优路径。结果在两种LCAs上一致，证明了该框架对其他与旋转相关的LCAs家族的适用性。
英文摘要
SIMON and SIMECK belong to a family of Lightweight Cryptographic Algorithms (LCAs) based on the Feistel block cipher, designed for Internet of Things (IoT) devices. As with all Feistel ciphers, they are susceptible to differential cryptanalysis, necessitating rigorous resilience evaluations. While state-of-the-art techniques leverage heuristics and sampling to improve efficiency, little work has applied Machine Learning (ML) guided Graph Representation Learning (GRL) to efficiently identify and visualise high-probability differential clusters. We address this gap by introducing an efficient feature engineering strategy that extracts four differential attributes from a partial Difference Distribution Table (pDDT), revealing structural information concealed in raw differential data. Utilising the enriched features, we construct and compare three ML-guided directed graphs for SIMON$32$ and SIMECK$32$ using K-Nearest Neighbour (KNN), Decision Trees (DT), and Random Forests (RF). To the best of our knowledge, our framework produces the first graph-based visualisation of the differential clustering effect, in which high-probability single-bit differentials form geometrically close clusters in the learned embedding. All three models achieve a precision of $1.0$ in identifying high-probability differentials, confirming zero false positives. KNN achieves the strongest cluster separation, the highest F1 score and the lowest graph construction time of approximately $2.3$ seconds, while DT and RF produce optimal paths with near-perfect regression. The results are consistent across both LCAs, demonstrating the applicability of the framework to other AND-rotation LCA families.
发表机构
- School of Computing, Mathematics and Engineering, Charles Sturt University（查尔斯斯特大学计算、数学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。
