As the fast development of IoT technology, various security solutions have to be considered when the corresponding solutions are being deployed. Due to the lightweight nature of the IoT devices such as the RFID tags and so on, traditional encryption schemes such as AES which are relatively heavy in the sense of operations cannot be applied here. Lightweight block ciphers have since become a default standard when considering security protections on such lightweight IoT devices. Compared with the security analysis approaches by taking advantage of the differential or linear cryptanalysis, the security margin of lightweight block ciphers can be further derived more accurately due to the small internal state. In this paper, we investigate the security margin of the lightweight block cipher structure especially the SPN design by taking advantage of the parallel computing power of modern GPU architecture. We show how to accelerate the computing of the statistical distinguisher, which is the crucial point for analyzing the security of the cipher design. Our proposed methods gain notable advantage against traditional CPU architecture in terms of time complexity and possess extensibility for other block ciphers.

随着物联网技术的快速发展，在部署相应的解决方案时必须考虑各种安全解决方案。由于诸如RFID标签之类的IoT设备的轻量级特性，在此处无法应用相对繁琐的传统加密方案（如AES）。从那时起，轻量级分组密码已成为考虑在此类轻量级IoT设备上进行安全保护时的默认标准。与利用差分或线性密码分析的安全性分析方法相比，由于内部状态小，因此可以更准确地导出轻量级分组密码的安全性裕度。在本文中，我们利用现代GPU架构的并行计算能力，研究了轻量级分组密码结构（特别是SPN设计）的安全裕度。我们展示了如何加速统计识别器的计算，这是分析密码设计安全性的关键点。在时间复杂度方面，我们提出的方法相对于传统的CPU体系结构具有明显的优势，并且对于其他分组密码具有可扩展性。