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Generating datasets for anomaly-based intrusion detection systems in IoT and industrial IoT networks

Generating datasets for anomaly-based intrusion detection systems in IoT and industrial IoT networks

Essop, Ismael ORCID: 0000-0002-5583-0306, Ribeiro, José C., Papaioannou, Maria, Zachos, Georgios, Mantas, Georgios ORCID: 0000-0002-8074-0417 and Rodriguez, Jonathan (2021) Generating datasets for anomaly-based intrusion detection systems in IoT and industrial IoT networks. Sensors, 21 (4):1528. ISSN 1424-8220 (Online) (doi:https://doi.org/10.3390/s21041528)

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Abstract

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”.

Item Type: Article
Uncontrolled Keywords: IoT, industrial IoT, benign datasets generation, malicious datasets generation, Cooja simulator, Contiki OS, anomaly-based intrusion detection
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Design, Manufacturing and Innovative Products Research Theme
Faculty of Engineering & Science > School of Engineering (ENN)
Last Modified: 10 Mar 2021 15:45
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/31390

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