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”.
Highlights
Despite the significant benefits that Internet of Things (IoT) and Industrial IoT (IIoT) networks bring to citizens, society, and industry, the fact that these networks incorporate a wide range of different communication technologies (e.g., WLANs, Bluetooth, and Zigbee) and types of nodes/devices, which are vulnerable to various types of security threats, raises many security and privacy challenges in IoT/IIoT-based systems
There is an urgent need for novel security mechanisms, such as accurate and efficient anomaly-based intrusion detection systems (AIDSs) tailored to the resource-constrained characteristics of IoT/IIoT networks, to be developed in order to address the pressing security challenges of IoT/IIoT networks with reasonable cost, in terms of processing and energy, before IoT/IIoT networks gain the trust of all involved stakeholders and reach their full potential in the market [1,2,3]
There is a lack of up-to-date, representative and well-structured IoT/IIoT-specific datasets that are publicly available to the research community and constitute benchmark datasets for training and evaluating machine learning (ML) models used in AIDSs for IoT/IIoT networks [4,5]
Summary
Despite the significant benefits that IoT and Industrial IoT (IIoT) networks bring to citizens, society, and industry, the fact that these networks incorporate a wide range of different communication technologies (e.g., WLANs, Bluetooth, and Zigbee) and types of nodes/devices (e.g., temperature/humidity sensors), which are vulnerable to various types of security threats, raises many security and privacy challenges in IoT/IIoT-based systems. There is an urgent need for comprehensive IoT/IIoT-specific datasets containing network-related information (e.g., packet-level information and flowlevel information) and events reflecting multiple benign and attack scenarios from current IoT/IIoT network environments, sensor measurement data, and information related to the behaviour of the IoT/IIoT devices deployed within the IoT/IIoT network for efficient and effective training and evaluation of AIDSs suitable for IoT/IIoT networks. It is clear that more comprehensive IoT/IIoT-specific datasets including events reflecting multiple benign and attack scenarios, sensor measurement data, network-related information, and information related to the behaviour of the IoT/IIoT devices are required to be generated and become publicly available to the research community so as to fill this significant research gap of lack of benchmark IoT/IIoT datasets and more accurate and efficient IoT/IIoT-specific AIDS to be developed. The perception doma2i.nT,harseasthAonwalynsiisnofFtihgeuIroeT/1II,ocTaNnebtweoprker(PceericvepetdioansDtohmeadine)vice layer in the ITU-T reference modelth[e1I5TT]Uh. -eATpsreertfcheerepentmicoenamdinoodmpealui[nr1,p5a]o.sAsshesotowhfentmihnaeiFnipgpeuurrrecpe1o,pscetaoinof bntheedppeoermcreceiavpietndionaissdttohome gdaiaenvtiihsceetorlagyaetrhienr data, the security challengdaetsa, itnhetsheicsurdityomchaalilnengtaesrginetthitsodofomraginetacrogleltetcotefodrgeIocTol/leIcIoteTd IdoTa/taIIoaTnddata and damage perception devicedsa,maasgeppreersceepnttioenddbeveilcoews, a.s presented below
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