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From Individual Decisions from Experience to Behavioral Game Theory: Lessons for Cybersecurity

  • Cleotilde Gonzalez
Conference paper
Part of the Advances in Information Security book series (ADIS, volume 100)

Abstract

This chapter discusses a central challenge arising from the success of modeling human behavior in making decisions from experience: our ability to scale these models up to explain non-cooperative team behavior in a dynamic cyber space. Computational models of human behavior based on the Instance-Based Learning Theory (IBLT) have been highly successful in representing and predicting individual’s behavior of decisions from experience. Recently, these IBL models have been also applied to represent a security analyst’s experience and cognitive characteristics that would result in accurate predictions of threat identification and cyber-attack detection. The IBL models derive predictions on the accuracy and timing of threat detection in a computer network (i.e., cyber situation awareness or cyberSA). This chapter summarizes the current state of models at the individual level, and it describes the challenges and potentials for extending them to address predictions in 2-player (i.e., defender and attacker) non-cooperative dynamic cybersecurity situations. The advancements that would potentially contribute to a more secure cyberspace are discussed.

Keywords

Network Event Dynamic Task Attack Strategy Task Situation Threat Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This research was a part of a Multidisciplinary University Research Initiative Award on Cyber Situation Awareness (MURI; #W911NF-09-1-0525) from Army Research Office to Cleotilde Gonzalez. We thank Hau-yu Wong for editing this manuscript.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Dynamic Decision Making Laboratory, Department of Social and Decision SciencesCarnegie Mellon UniversityPittsburghUSA

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