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AbstractMultiple 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.

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.

Introduction / Michael L. Woodson -- 1. A proposed business-oriented approach to cyber / David Leigh -- 2. A practical approach to developing a cybersecurity programme / David Fairman -- 3. Regulations, compliance and cyber risk management / Don Anderson -- 4. The role of cyber risk in the organisation / Jack Jones -- 5. The evolution of the cyber risk role within the three lines of defence / Alexander Abramov -- 6. Quantifying cyber risk / Jack Jones -- 7. Leadership and culture: the foundations of cyber-risk management / Brett T. Williams -- 8. Understanding the cyber risk landscape: an integrated framework / Mark Cooke -- 9. The transformation of information security: new threats and vulnerabilities / Adrian Davis -- 10. Cybersecurity metrics: the good, the bad and the ugly / Adrian Davis -- 11. Third-party risk management / Tom Garrubba -- 12. Cybersecurity's neighbourhood watch: the strength of information sharing / Bill Nelson -- 13. Cyber risks in business continuity management and supply chain resilience for financial institutions / Alexander Ellrodt -- 14. Cybersecurity threats to the critical infrastructure / Brian Lozada -- 15. The true meaning of cyber incident response / Henry Jiang -- 16. Cyber risk: where we have been, where we are, and where we are going / Mark Clancy

"This book compiles innovative research from international professionals discussing the opportunities and challenges of the new era of online business and outlining updated discourse for business analytics techniques, strategies for data storage, and encryption in emerging markets"--

This book provides a brief and general introduction to cybersecurity and cyber-risk assessment. Not limited to a specific approach or technique, its focus is highly pragmatic and is based on established international standards (including ISO 31000) as well as industrial best practices. It explains how cyber-risk assessment should be conducted, which techniques should be used when, what the typical challenges and problems are, and how they should be addressed. The content is divided into three parts. First, part I provides a conceptual introduction to the topic of risk management in general and to cybersecurity and cyber-risk management in particular. Next, part II presents the main stages of cyber-risk assessment from context establishment to risk treatment and acceptance, each illustrated by a running example. Finally, part III details four important challenges and how to reasonably deal with them in practice: risk measurement, risk scales, uncertainty, and low-frequency risks with high consequence. The target audience is mainly practitioners and students who are interested in the fundamentals and basic principles and techniques of security risk assessment, as well as lecturers seeking teaching material. The book provides an overview of the cyber-risk assessment process, the tasks involved, and how to complete them in practice.