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Artificially Intelligent Cyber Security: Reducing Risk and Complexity

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Advances in Artificial Intelligence and Applied Cognitive Computing

Abstract

Historically, research shows analysis, characterization, and classification of complex heterogeneous non-linear systems; and interactions have been difficult to accurately understand and effectively model. Synonymously, exponential growth of Internet of Things (IoT), cyber physical systems, and the litter of current accidental and unscrupulous cyber events portray an ever-challenging security environment wrought with complexity, ambiguity, and non-linearity, thus providing significant incentive to industry and academia toward advanced, predictive solutions to reduce persistent global threats. Recent advances in artificial intelligence (AI) and information theoretic methods (ITM) are benefitting disciplines struggling with learning from rapidly increasing data volume, velocity, and complexity. Research shows axiomatic design (AD) providing design and datum disambiguation for complex systems utilizing information content reduction. Therefore, we propose a transdisciplinary AD, AI/ML, ITM approach combining axiomatic design with advanced, novel, and adaptive machine-based learning techniques. We show how to significantly reduce risks and complexity by improving cyber system adaptiveness, enhancing cyber system learning, and increasing cyber system prediction and insight potential where today context is sorely lacking. We provide an approach for deeper contextual understanding of disjointed cyber events by improving knowledge density (KD) (how much we know about a given event) and knowledge fidelity (KF) (how well do we know) ultimately improving decision mitigation quality and autonomy. We improve classification and understanding of cyber data and reduce system non-linearity and cyber threat risk, thereby increasing efficiency by reducing labor and system costs, and “peace of mind.”

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Southern Methodist University, Dallas, TX, USA

    John N. Carbone

  2. Colorado Engineering, Inc., Colorado Springs, CO, USA

    James A. Crowder

Authors
  1. John N. Carbone
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  2. James A. Crowder
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Corresponding author

Correspondence to James A. Crowder .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Georgia, Athens, GA, USA

    Hamid R. Arabnia

  2. Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, Canada

    Ken Ferens

  3. Business Administration, University of Oviedo, Oviedo, Asturias, Spain

    David de la Fuente

  4. Institute of Informatics Problems, The Russian Academy of Sciences, Moscow, Russia

    Elena B. Kozerenko

  5. Technology and Information systems, Universidad de Castilla La Mancha, Ciudad Real, Ciudad Real, Spain

    José Angel Olivas Varela

  6. Facultad de Informática - CIC PBA, Universidad Nacional de La Plata, La Plata, Argentina

    Fernando G. Tinetti

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Carbone, J.N., Crowder, J.A. (2021). Artificially Intelligent Cyber Security: Reducing Risk and Complexity. In: Arabnia, H.R., Ferens, K., de la Fuente, D., Kozerenko, E.B., Olivas Varela, J.A., Tinetti, F.G. (eds) Advances in Artificial Intelligence and Applied Cognitive Computing. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-70296-0_38

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  • DOI: https://doi.org/10.1007/978-3-030-70296-0_38

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  • Publisher Name: Springer, Cham

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