A malware detection algorithm that can be embedded in IoT edge computing is proposed in this study and validated using an emulator. This algorithm, with a pattern match accelerator, reduces the computing cost while maintaining a relatively high detection accuracy. For autonomous driving, complicated IoT edge computing must have a huge amount of embedded program codes. In such a situation, the invasion of malware can lead to compromised cybersecurity. In this study, a pattern match accelerator is implemented for such issues, thereby offering IoT edge computing that detects malware automatically. Edge computing is designed to apply simply structural level analysis algorithms using HLAC mask pattern. We developed a pseudo‐emulator system environment and conducted performance confirmation of the proposed technique using 641 chosen samples from six types of malware families. The algorithm's efficiencies demonstrated an identification performance of approximately 80%. In comparison to characteristic extraction using AI, the computing cost was reduced and these processes enable edge computing with high cybersecurity features.

中文翻译：

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利用模式匹配加速器的物联网边缘网络安全研究

这项研究提出了一种可以嵌入到物联网边缘计算中的恶意软件检测算法，并使用仿真器对其进行了验证。具有模式匹配加速器的该算法在保持相对较高的检测精度的同时降低了计算成本。对于自动驾驶，复杂的IoT边缘计算必须具有大量的嵌入式程序代码。在这种情况下，恶意软件的入侵可能会导致网络安全性受损。在这项研究中，针对此类问题实施了模式匹配加速器，从而提供了可自动检测恶意软件的IoT边缘计算。边缘计算旨在使用HLAC掩模图案简单地应用结构级分析算法。我们开发了伪仿真器系统环境，并使用从六种恶意软件家族中选择的641个样本对提出的技术进行了性能确认。该算法的效率证明了大约80％的识别性能。与使用AI进行特征提取相比，降低了计算成本，并且这些过程使具有高网络安全性功能的边缘计算成为可能。