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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
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Radanliev, Petar, De Roure, David, Page, Kevin, Van Kleek, Max, Santos, Omar, Maddox, La’Treall, Burnap, Peter, Anthi, Eirini and Maple, Carsten (2020) 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, 2 . pp. 219-230. doi:10.1007/s42797-021-00025-1
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Official URL: http://dx.doi.org/10.1007/s42797-021-00025-1
Abstract
Multiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. 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. 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.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TJ Mechanical engineering and machinery |
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| Divisions: | Faculty of Science > WMG (Formerly the Warwick Manufacturing Group) | |||||||||
| Library of Congress Subject Headings (LCSH): | Artificial intelligence , Adaptive control systems , Computer networks -- Security measures -- Government policy, Cyber intelligence (Computer security) -- Computer programs, Machine learning, Mars (Planet) -- Colonization, Mars (Planet) -- Exploration -- Data processing | |||||||||
| Journal or Publication Title: | Safety in Extreme Environments | |||||||||
| Publisher: | Springer Nature Switzerland AG | |||||||||
| ISSN: | 2524-8170 | |||||||||
| Official Date: | October 2020 | |||||||||
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| Volume: | 2 | |||||||||
| Page Range: | pp. 219-230 | |||||||||
| DOI: | 10.1007/s42797-021-00025-1 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access | |||||||||
| RIOXX Funder/Project Grant: |
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