Artificial Intelligence, Machine Learning and Real-time Probabilistic Data for Cyber Risk (Super) – Forecasting

11 Pages Posted: 7 Apr 2020

See all articles by Petar Radanliev

Petar Radanliev

Department of Engineering Science

David C. De Roure

University of Oxford

Kevin Page

affiliation not provided to SSRN

Max Van Kleek

University of Oxford - Department of Computer Science

Rafael Mantilla Montalvo

Cisco Research Centre

Omar Santos

Cisco Research Centre

La’Treall Maddox

Cisco Systems

Stacy Cannady

Cisco Systems

Pete Burnap

Cardiff University

Eirini Anthi

affiliation not provided to SSRN

Carsten Maple

affiliation not provided to SSRN

Date Written: March 12, 2020

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, IoT networks, cyber risk analytics, deep learning algorithms, risk models, edge computing

JEL Classification: C02, C45, C6, C6 0, C61, C62

Suggested Citation

Radanliev, Petar and Roure, David C. De and Page, Kevin and Van Kleek, Max and Montalvo, Rafael Mantilla and Santos, Omar and Maddox, La’Treall and Cannady, Stacy and Burnap, Pete and Anthi, Eirini and Maple, Carsten, Artificial Intelligence, Machine Learning and Real-time Probabilistic Data for Cyber Risk (Super) – Forecasting (March 12, 2020). Available at SSRN: https://ssrn.com/abstract=3553255 or http://dx.doi.org/10.2139/ssrn.3553255

Petar Radanliev (Contact Author)

Department of Engineering Science ( email )

7 Keble Road
Oxford, OX1 3QG
United Kingdom
7917541847 (Phone)
7917541847 (Fax)

HOME PAGE: http://www.oerc.ox.ac.uk/people/PetarR

David C. De Roure

University of Oxford ( email )

Mansfield Road
Oxford, Oxfordshire OX1 4AU
United Kingdom

Kevin Page

affiliation not provided to SSRN

Max Van Kleek

University of Oxford - Department of Computer Science ( email )

Wolfson Building, Parks Road
Oxford
United Kingdom

Rafael Mantilla Montalvo

Cisco Research Centre ( email )

United States

Omar Santos

Cisco Research Centre ( email )

United States

La’Treall Maddox

Cisco Systems ( email )

San Jose, CA 95134
United States

Stacy Cannady

Cisco Systems ( email )

San Jose, CA 95134
United States

Pete Burnap

Cardiff University ( email )

Aberconway Building
Colum Drive
Cardiff, Wales CF10 3EU
United Kingdom

Eirini Anthi

affiliation not provided to SSRN

Carsten Maple

affiliation not provided to SSRN

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