Sensors, Vol. 21, Pages 1528: Generating Datasets for Anomaly-Based Intrusion Detection Systems in IoT and Industrial IoT Networks

Sensors, Vol. 21, Pages 1528: Generating Datasets for Anomaly-Based Intrusion Detection Systems in IoT and Industrial IoT Networks Sensors doi: 10.3390/s21041528 Authors: Ismael Essop José C. Ribeiro Maria Papaioannou Georgios Zachos Georgios Mantas Jonathan Rodriguez 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...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research