PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Artificial Intelligence, Machine Learning and Real-time Probabilistic Data for Cyber Risk (Super) -forecasting: Red Teaming the Connected World (RETCON)
Version 1
: Received: 12 March 2020 / Approved: 12 March 2020 / Online: 12 March 2020 (14:24:54 CET)
Version 2
: Received: 9 April 2021 / Approved: 12 April 2021 / Online: 12 April 2021 (12:18:14 CEST)
How to cite:
Radanliev, P. Artificial Intelligence, Machine Learning and Real-time Probabilistic Data for Cyber Risk (Super) -forecasting: Red Teaming the Connected World (RETCON). Preprints2020, 2020030217. https://doi.org/10.20944/preprints202003.0217.v1.
Radanliev, P. Artificial Intelligence, Machine Learning and Real-time Probabilistic Data for Cyber Risk (Super) -forecasting: Red Teaming the Connected World (RETCON). Preprints 2020, 2020030217. https://doi.org/10.20944/preprints202003.0217.v1.
Cite as:
Radanliev, P. Artificial Intelligence, Machine Learning and Real-time Probabilistic Data for Cyber Risk (Super) -forecasting: Red Teaming the Connected World (RETCON). Preprints2020, 2020030217. https://doi.org/10.20944/preprints202003.0217.v1.
Radanliev, P. Artificial Intelligence, Machine Learning and Real-time Probabilistic Data for Cyber Risk (Super) -forecasting: Red Teaming the Connected World (RETCON). Preprints 2020, 2020030217. https://doi.org/10.20944/preprints202003.0217.v1.
Abstract
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.
Keywords
Artificial intelligence; machine learning; real-time probabilistic data; for cyber risk; super forecasting; red teaming;
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
