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
Creators: name: Radanliev, Petar
Publisher: name: figshare
Year: 2021
Subjects: subject: 80303 Computer System Security
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: 160206 Private Policing and Security Services
subject: FOS: Law
subject: FOS: Law
subject: 91507 Risk Engineering (excl. Earthquake Engineering)
subject: FOS: Other engineering and technologies
subject: FOS: Other engineering and technologies
subject: 150205 Investment and Risk Management
subject: FOS: Economics and business
subject: FOS: Economics and business
subject: Artificial Intelligence and Image Processing
subject: 80102 Artificial Life
subject: 170203 Knowledge Representation and Machine Learning
subject: FOS: Psychology
subject: FOS: Psychology
subject: 20199 Astronomical and Space Sciences not elsewhere classified
subject: FOS: Physical sciences
subject: FOS: Physical sciences
subject: Aerospace Engineering
subject: FOS: Mechanical engineering
subject: FOS: Mechanical engineering
subject: 90108 Satellite, Space Vehicle and Missile Design and Testing
subject: 20102 Astronomical and Space Instrumentation
subject: 90102 Aerospace Materials
subject: 90103 Aerospace Structures
subject: 90199 Aerospace Engineering not elsewhere classified
subject: Space Science
subject: 200406 Language in Time and Space (incl. Historical Linguistics, Dialectology)
subject: FOS: Languages and literature
subject: FOS: Languages and literature
subject: 20109 Space and Solar Physics
subject: 80199 Artificial Intelligence and Image Processing not elsewhere classified
subject: 150301 Business Information Management (incl. Records, Knowledge and Information Management, and Intelligence)
subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: FOS: Earth and related environmental sciences
subject: 50204 Environmental Impact Assessment
subject: 39901 Environmental Chemistry (incl. Atmospheric Chemistry)
subject: FOS: Chemical sciences
subject: FOS: Chemical sciences
subject: 111705 Environmental and Occupational Health and Safety
subject: FOS: Health sciences
subject: FOS: Health sciences
subject: Environmental Science
subject: 90702 Environmental Engineering Modelling
subject: FOS: Environmental engineering
subject: FOS: Environmental engineering
subject: 90701 Environmental Engineering Design
subject: 160507 Environment Policy
subject: FOS: Political science
subject: FOS: Political science
subject: 140205 Environment and Resource Economics

description: 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 andArtificial 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.