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Cognitive Modeling for Personalized, Adaptive Signaling for Cyber Deception

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Cyber Deception

Part of the book series: Advances in Information Security ((ADIS,volume 89))

Abstract

This chapter presents an overview of the use of cognitive models for personalized adaptive signaling for cyber deception. It provides a general introduction to cognitive modeling techniques and concepts, including general goals, capabilities and limitations, cognitive architectures, and instance-based learning. It establishes that cognitive models’ reliance on generative mechanisms has predictive capabilities beyond those of purely data-driven techniques such as machine learning that can be used to evaluate the effectiveness of cyber defense techniques without requiring full implementation and test. Cognitive models can account for the entire range of human performance, including levels of expertise and individual differences. Techniques such as knowledge-tracing and model-tracing can align a specific cognitive model against an individual behavior trace, enabling personalized interventions. Because cognitive models are analytically tractable, they can guide, inform, and optimize the design of cyber deception techniques. We illustrate these concepts using an insider attacking game meant to abstract the dynamics and decision-making characteristics of real-world cyber defense. Finally, we conclude by discussing future research directions in the development and application of cognitive models to cyber deception for defense.

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Acknowledgments

This research was sponsored by the Army Research Office and accomplished under MURI Grant Number W911NF-17-1-0370.

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Authors and Affiliations

  1. Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA

    Christian Lebiere & Edward A. Cranford

  2. Social and Decision Sciences Department, Carnegie Mellon University, Pittsburgh, PA, USA

    Palvi Aggarwal & Cleotilde Gonzalez

  3. USC Center for AI in Society, University of Southern California, Los Angeles, CA, USA

    Sarah Cooney

  4. Center for Research in Computation and Society, Harvard University, Boston, MA, USA

    Milind Tambe

Authors
  1. Christian Lebiere
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  2. Edward A. Cranford
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  3. Palvi Aggarwal
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  4. Sarah Cooney
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  5. Milind Tambe
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  6. Cleotilde Gonzalez
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Corresponding author

Correspondence to Christian Lebiere .

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Editors and Affiliations

  1. Arizona State University, Tempe, AZ, USA

    Tiffany Bao

  2. Harvard University, Cambridge, MA, USA

    Milind Tambe

  3. Army Research Office, Adelphi, MD, USA

    Cliff Wang

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Lebiere, C., Cranford, E.A., Aggarwal, P., Cooney, S., Tambe, M., Gonzalez, C. (2023). Cognitive Modeling for Personalized, Adaptive Signaling for Cyber Deception. In: Bao, T., Tambe, M., Wang, C. (eds) Cyber Deception. Advances in Information Security, vol 89. Springer, Cham. https://doi.org/10.1007/978-3-031-16613-6_4

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  • DOI: https://doi.org/10.1007/978-3-031-16613-6_4

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  • Online ISBN: 978-3-031-16613-6

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