What is it about?
This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing.
Featured Image
Photo by Shot by Cerqueira on Unsplash
Why is it important?
The advancements of artificial intelligence in industrial automation, triggers questions on safety and security, and whether AI has enhanced security or increased the cyber risks.
Perspectives
The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.
Dr Petar Radanliev
University of Oxford
Read the Original
This page is a summary of: 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, Safety in Extreme Environments, October 2020, Springer Science + Business Media,
DOI: 10.1007/s42797-021-00025-1.
You can read the full text:
Contributors
The following have contributed to this page
