Artificial intelligence and machine learning in dynamic cyber risk analytics at the edge

• DOI: 10.1007/s42452-020-03559-4
• Publication Date: 2020-10-06T23:04:29Z
• Authors: Petar Radanliev; David De Roure; Rob Walton; Max Van Kleek; Rafael Mantilla M...; La’Treall Maddox; Omar Santos; Peter Burnap; Eirini Anthi
• Keywords: Internet of things; Artificial intelligence; Technology; 0209 industrial biotechnology; Cyber-physical systems; Science; T; Q; 02 engineering and technology; Artificial cognition; Automatic anomaly detection system; Machine learning

AbstractWe explore the potential and practical challenges in the use of artificial intelligence (AI) in cyber risk analytics, for improving organisational resilience and understanding cyber risk. The research is focused on identifying the role of AI in connected devices such as Internet of Things (IoT) devices. Through literature review, we identify wide ranging and creative methodologies for cyber analytics and explore the risks of deliberately influencing or disrupting behaviours to socio-technical systems. This resulted in the modelling of the connections and interdependencies between a system's edge components to both external and internal services and systems. We focus on proposals for models, infrastructures and frameworks of IoT systems found in both business reports and technical papers. We analyse this juxtaposition of related systems and technologies, in academic and industry papers published in the past 10 years. Then, we report the results of a qualitative empirical study that correlates the academic literature with key technological advances in connected devices. The work is based on grouping future and present techniques and presenting the results through a new conceptual framework. With the application of social science's grounded theory, the framework details a new process for a prototype of AI-enabled dynamic cyber risk analytics at the edge.