Impact of Cyber Risk at the Edge: Cyber Risk Analytics and Artificial Intelligence (CRatE)

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CRatE researches the role of artificial intelligence and machine learning to design a self-adapting system for predictive cyber risk analytics that can form an automatic anomaly detection system. The team aims to develop a higher Technology Readiness Level (TRL) for dynamic analytics of cyber-attack threat event frequencies, that enable predicting the cyber risk loss magnitude.

Lack of probabilistic data leads to qualitative cyber risk assessment approaches. This leads to speculative assumption. Quantitative risk impact estimation based on real-time data is needed for making decisions on cybersecurity, cyber risk and cyber insurance. Without dynamic real-time risk data and cyber risk analytics enhanced with artificial intelligence and machine learning cognition and data collection mechanisms, these estimations can be outdated and imprecise.

CRatE is using a series of red teaming events to understand how users and autonomous systems interact to ensure system resilience and how to stress-test such systems for complex attacks. The team is developing a demonstration project that would exhibit how such integration for dynamic real-time cyber risk analytics would work in practice.

Publications

[1]      Radanliev, Petar., De Roure, David., Page, Kevin., Van Kleek, Max., Santos, Omar., Maddox, La’Treall., Burnap, Peter., … Maple, Carsten, “Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars,” Saf. Extrem. Environ., pp. 1–12, Feb. 2021. https://link.springer.com/article/10.1007/s42797-021-00025

[2]      Radanliev, Petar., and De Roure, David, “Covid-19 and cloud technologies: All-Cloud IT Operating Model for pandemic management”, IEEE Future Directions, Jan-2021. [Online]. Available: https://cmte.ieee.org/futuredirections/tech-policy-ethics/january-2021/covid-19-and-cloud-technologies-all-cloud-it-operating-model-for-pandemic-management/

[3]      Radanliev, Petar., De Roure, David., and Van Kleek, Max, “Cyber-risks from Using IoT Devices for Managing COVID-19″, IEEE IoT Newsletter, 2021. https://iot.ieee.org/newsletter/january-2021/cyber-risks-from-using-iot-devices-for-managing-covid-19

[4]      Radanliev, Petar., De Roure, David., Van Kleek, Max., Ani, Uchenna., Burnap, Pete., Anthi, Eirini., Nurse, Jason R.C., … Maddox, La’Treall T., “Dynamic real-time risk analytics of uncontrollable states in complex internet of things systems: cyber risk at the edge,” Environ. Syst. Decis., vol. 1, pp. 1–12, Nov. 2020. https://doi.org/10.1007/s10669-020-09792-x

[5]      Radanliev, Petar., De Roure, David., Walton, Rob., Van Kleek, Max., Montalvo, Rafael Mantilla., Maddox, La’Treall., Santos, Omar., … Anthi, Eirini, “Artificial intelligence and machine learning in dynamic cyber risk analytics at the edge,” SN Appl. Sci., vol. 2, no. 11, pp. 1–8, Nov. 2020. https://doi.org/10.1007/s42452-020-03559-4

[6]      Radanliev, Petar., De Roure, David., Van Kleek, Max., Santos, Omar., and Ani, Uchenna, “Artificial intelligence in cyber physical systems,” AI Soc., vol. 1, pp. 1–14, Aug. 2020. https://doi.org/10.1007/s00146-020-01049-0

[7]      Radanliev, Petar., De Roure, David., Walton, Rob., Van Kleek, Max., Montalvo, Rafael Mantilla., Santos, Omar., Maddox, La’Treall T., and Cannady, Stacy, “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, vol. 11, no. 3. Springer, pp. 311–332, 01-Sep-2020. https://doi.org/10.1007/s13167-020-00218-x

Dr Petar Radanliev has written a blog post about this publication here: https://petras-iot.org/update/covid-19-what-have-we-learned-the-rise-of-social-machines-and-connected-devices-in-pandemic-management/

[8]      Radanliev, Petar., De Roure, David., and Walton, Rob, “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 Metab. Syndr. Clin. Res. Rev., vol. 14, no. 5, pp. 1121–1132, Sep. 2020. https://doi.org/10.1016/j.dsx.2020.06.063

[9]      Radanliev, Petar., and De Roure, David, “Cloud technologies: All-Cloud IT Operating Model,” IEEE Future Directions, 2020. https://cmte.ieee.org/futuredirections/tech-policy-ethics/september-2020/cloud-technologies-all-cloud-it-operating-model/

[10]      Radanliev, Petar, “Cyber Risk from IoT Devices and Networks,” IEEE Internet of Things Newsletter, 2020. https://iot.ieee.org/newsletter/july-2020/cyber-risk-from-iot-devices-and-networks