Essop Ismael, Ribeiro José C, Papaioannou Maria, Zachos Georgios, Mantas Georgios, Rodriguez Jonathan
Faculty of Engineering and Science, University of Greenwich, Chatham Maritime ME4 4TB, UK.
Instituto de Telecomunicações, Aveiro 3810-193, Portugal.
Sensors (Basel). 2021 Feb 23;21(4):1528. doi: 10.3390/s21041528.
Over the past few years, we have witnessed the emergence of Internet of Things (IoT) and Industrial IoT networks that bring significant benefits to citizens, society, and industry. However, their heterogeneous and resource-constrained nature makes them vulnerable to a wide range of threats. Therefore, there is an urgent need for novel security mechanisms such as accurate and efficient anomaly-based intrusion detection systems (AIDSs) to be developed before these networks reach their full potential. Nevertheless, there is a lack of up-to-date, representative, and well-structured IoT/IIoT-specific datasets which are publicly available and constitute benchmark datasets for training and evaluating machine learning models used in AIDSs for IoT/IIoT networks. Contribution to filling this research gap is the main target of our recent research work and thus, we focus on the generation of new labelled IoT/IIoT-specific datasets by utilising the Cooja simulator. To the best of our knowledge, this is the first time that the Cooja simulator is used, in a systematic way, to generate comprehensive IoT/IIoT datasets. In this paper, we present the approach that we followed to generate an initial set of benign and malicious IoT/IIoT datasets. The generated IIoT-specific information was captured from the Contiki plugin "powertrace" and the Cooja tool "Radio messages".
在过去几年里，我们见证了物联网（IoT）和工业物联网网络的出现，它们给公民、社会和工业带来了巨大的好处。然而，它们异构且资源受限的特性使它们容易受到各种威胁。因此，迫切需要开发新颖的安全机制，如准确高效的基于异常的入侵检测系统（AIDS），以便在这些网络充分发挥其潜力之前投入使用。尽管如此，目前缺乏最新的、具有代表性的且结构良好的特定于物联网/工业物联网的数据集，这些数据集应是公开可用的，并构成用于训练和评估物联网/工业物联网网络入侵检测系统中使用的机器学习模型的基准数据集。填补这一研究空白是我们近期研究工作的主要目标，因此，我们专注于利用Cooja模拟器生成新的带标签的特定于物联网/工业物联网的数据集。据我们所知，这是首次系统地使用Cooja模拟器来生成全面的物联网/工业物联网数据集。在本文中，我们展示了我们为生成初始的良性和恶意物联网/工业物联网数据集所采用的方法。生成的特定于工业物联网的信息是从Contiki插件“powertrace”和Cooja工具“无线电消息”中捕获的。
