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Biography
Dr Petar Radanliev obtained his Ph.D at University of Wales in 2014 and continued with Post-Doctoral research at Imperial College London, Massachusetts Institute of Technology, the University of Cambridge and the University of Oxford.
Dr Radanliev was a Prince of Wales Innovation Scholar - 2010/2013 and Fulbright Visiting Fellow - 2017.
His current research is oriented around identifying a dynamic and self-adapting system supported with Artificial Intelligence/Machine Learning and real-time intelligence for predictive cyber risk analytics, integrated into a cognition engine for edge computing which uses machine learning to automate anomaly detection.
This research work is to secure the edge by predicting cyber risk loss magnitude through dynamic analytics of cyber-attack threat event frequencies. Significantly the challenges Dr Radanliev's project addresses are socio-technical, relating strongly to technology, regulation, economics, interventions, and directly relates to industries and their supply chains and control systems. For example he is investigating the perceptions of risk and trustworthiness that emerge as a result of machine agency, which interact with regulation, standards and policy on the one hand and design and engineering on the other, spanning the physical and behavioural sciences.
This research is funded by the Cisco Research Centre and by the PETRAS|EPSRC National Centre of Excellence for loT Systems Cybersecurity.
Research Interests
- Dynamic and self-adapting cyber-risk management system
- AI/ML and real-time intelligence for predictive cyber risk analytics
- Cognition engine in edge computing for automated anomaly detection
- IoT real-time intelligence for predictive cyber risk analytics
- Real time analytics
- Cyber risk from artificial intelligence
- Cyber risk in National Critical Infrastructure
- Cybersecurity of the internet of things
- Cyber-risk from the internet of things
- Economic impact from artificial intelligence and industry 4.0.
Current Projects
- Cisco Research Centre - Cyber Risk Analytics and Artificial Intelligence
- PETRAS/EPSRC project - Impact of Cyber Risk (CRatE)
Related Academics
Publications
- Radanliev, P. et al. (2020) Artificial intelligence in cyber physical systems, AI and Society. Springer, 1, p. 3. doi: 10.1007/s00146-020-01049-0
- Radanliev, P. et al. (2020) COVID-19 what have we learned? The rise of social machines and connected devices in pandemic management following the concepts of predictive, preventive and personalized medicine EPMA Journal. Springer, pp. 311–332. doi: 10.1007/s13167-020-00218-x
- Radanliev, P. et al. (2020) Cyber risk at the edge: current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains, Cybersecurity, Springer Nature, 3(13), pp. 1–21. doi: 10.1186/s42400-020-00052-8
- Radanliev, P., De Roure, D. and Walton, R. (2020) Data mining and analysis of scientific research data records on Covid-19 mortality, immunity, and vaccine development - In the first wave of the Covid-19 pandemic, Diabetes and Metabolic Syndrome: Clinical Research and Reviews. Elsevier Ltd, 14(5), pp. 1121–1132. doi: 10.1016/j.dsx.2020.06.063.
- Radanliev, P. et al. (2020) Future developments in standardisation of cyber risk in the Internet of Things (IoT), SN Applied Sciences. Springer Science and Business Media LLC, 2(2), pp. 1–16. doi: 10.1007/s42452-019-1931-0.
- Radanliev, P., De Roure, D. C., Nicolescu, R., Huth, M., Montalvo, R. M., Cannady, S., & Burnap, P. (2018). Future developments in cyber risk assessment for the internet of things. Computers in Industry, 102, 14–22.
- Radanliev, P., De Roure, C. D., Nurse, .R.C., Nicolescu, R., Huth, M., Cannady, C., … Cannady, S. (2018). Integration of Cyber Security Frameworks, Models and Approaches for Building Design Principles for the Internet-of-things in Industry 4.0 In Living in the Internet of Things: Cybersecurity of the IoT - 2018 (Vol. 2018). London: IET.
- Radanliev, P., De Roure, D., Cannady, S., Montalvo, R. M., Nicolescu, R., & Huth, M. (2018). Economic impact of IoT cyber risk - analysing past and present to predict the future developments in IoT risk analysis and IoT cyber insurance In Living in the Internet of Things: Cybersecurity of the IoT - 2018 (Vol. 2018). London: Institution of Engineering and Technology.
- Nurse, J. R. C., Radanliev, P., Creese, S., & De Roure, C. D. (2018). If you can’t understand it, you can’t properly assess it! The reality of assessing security risks in Internet of Things systems In Living in the Internet of Things: Cybersecurity of the IoT - 2018. Institution of Engineering and Technology.
- Nicolescu, R., Huth, M., Radanliev, P., De Roure, D., & Nicolescu, R. (2018). Mapping the values of IoT Journal of Information Technology, 1–16.
- Nurse, J. R. C., Radanliev, P., Creese, S., & De Roure, D. (2018). If you can’t understand it, you can’t properly assess it! The reality of assessing security risks in Internet of Things systems In Living in the Internet of Things: Cybersecurity of the IoT - 2018 (pp. 1–9). Institution of Engineering and Technology.
- Radanliev, P. (2016). Supply Chain Systems Architecture and Engineering Design: Green-field Supply Chain Integration. Operations and Supply Chain Management: An International Journal, 9(1). Retrieved from DOI:10.31387/oscm0230158
- Radanliev, P. (2015). Green-field Architecture for Sustainable Supply Chain Strategy Formulation International Journal of Supply Chain Management, 4(2).
- Radanliev, P. (2015). Architectures for Green-Field Supply Chain Integration Journal of Supply Chain and Operations Management, 13(2).
- Radanliev, P. (2015). Engineering Design Methodology for Green-Field Supply Chain Architectures Taxonomic Scheme Journal of Operations and Supply Chain Management, 8(2), 52-66.
- Radanliev, P., Rowlands, H., and Thomas A.J., (2014) ‘Supply Chain Paradox: Green-field Architecture for Sustainable Strategy Formulation’, SDM’2014 International Conference on Sustainable Design and Manufacturing, Cardiff, Wales, UK, 28-30 April 2014, Future Press Technology, pp. 839-851, ISBN 9780956151667.
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