The SOfIoTS project aims to understand and develop cybersecurity attributes for existing Industrial Internet of Things (IIoT) network ontologies to include machine learning at the Edge. The project’s planned outcomes will result in publications on which further academic endeavor can be built, whilst contributing to the understanding and methodologies of data security and assurance in Building Management Systems (BMS) in sensitive public buildings.
The convergence of building services with IoT (Internet of Things) and machine learning technologies can increase cybersecurity risks for organisations that can impact their bottom line, cause reputational damage and even create safety issues resulting in loss of life. The SOfIoTS project looks to answer some of these issues by consistently assuring appropriate cybersecurity to improve mission functionality and to implement local security features in BMS.
The SOfIoTS project, in partnership with National Physical Laboratory (NPL), Cube Controls, RITICS, 4D-SIG and PETRAS SRF1 project, ELLIoTT, will undertake a comprehensive review of IoT Network Ontologies to identify gaps in security provision, particularly with respect to industrial, buildings and utilities control applications. This will draw upon experience and review data from RITICS and ELLIoTT. The project will look at the Semantic Sensor Network (SSN) Ontology (w3.org), to include local machine learning functionality, and to provide methods for mapping abstract representations to specific ‘in context’ configurations of application. The project will also create a dialogue to promote delivery and impact through RITICS and the 4D-SIG to the UK Digital Twins programme and to the Centre for Digital Built Britain.