Visible to the public Biblio

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2016
Robert St. Amant, David L. Roberts.  2016.  Natural Interaction for Bot Detection. IEEE Internet Computing. 20(4):69–73.

Bot detection - identifying a software program that's using a computer system – is an increasingly necessary security task. Existing solutions balance proof of human identity with unobtrusiveness in users' workflows. Cognitive modeling and natural interaction might provide stronger security and less intrusiveness.

Ignacio X. Dominguez, Prairie Rose Goodwin, David L. Roberts, Robert St. Amant.  2016.  Human Subtlety Proofs: Using Computer Games to Model Cognitive Processes for Cybersecurity. International Journal of Human–Computer Interaction.

This article describes an emerging direction in the intersection between human-computer interaction and cognitive science: the use of cognitive models to give insight into the challenges of cybersecurity. The article gives a brief overview of work in different areas of cybersecurity where cognitive modeling research plays a role, with regard to direct interaction between end users and computer systems and with regard to the needs of security analysts working behind the scenes. The problem of distinguishing between human users and automated agents (bots) interacting with computer systems is introduced, as well as ongoing efforts toward building Human Subtlety Proofs, persistent and unobtrusive windows into human cognition with direct application to cybersecurity. Two computer games are described, proxies to illustrate different ways in which cognitive modeling can potentially contribute to the development of HSPs and similar cybersecurity applications.

Ignacio X. Dominguez, Prairie Rose Goodwin, David L. Roberts, Robert St. Amant.  2016.  Human Subtlety Proofs: Using Computer Games to Model Cognitive Processes for Cybersecurity. International Journal of Human–Computer Interaction. :null.

AbstractThis article describes an emerging direction in the intersection between human-computer interaction and cognitive science: the use of cognitive models to give insight into the challenges of cybersecurity. The article gives a brief overview of work in different areas of cybersecurity where cognitive modeling research plays a role, with regard to direct interaction between end users and computer systems and with regard to the needs of security analysts working behind the scenes. The problem of distinguishing between human users and automated agents (bots) interacting with computer systems is introduced, as well as ongoing efforts toward building Human Subtlety Proofs, persistent and unobtrusive windows into human cognition with direct application to cybersecurity. Two computer games are described, proxies to illustrate different ways in which cognitive modeling can potentially contribute to the development of HSPs and similar cybersecurity applications.

Robert St. Amant, David L. Roberts.  2016.  Natural Interaction for Bot Detection. IEEE Internet Computing. July/August

Bot detection - identifying a software program that's using a computer system -- is an increasingly necessary security task. Existing solutions balance proof of human identity with unobtrusiveness in users' workflows. Cognitive modeling and natural interaction might provide stronger security and less intrusiveness.

2015
Ignacio X. Dominguez, Alok Goel, David L. Roberts, Robert St. Amant.  2015.  Detecting Abnormal User Behavior Through Pattern-mining Input Device Analytics. Proceedings of the 2015 Symposium and Bootcamp on the Science of Security (HotSoS-15).
Robert St. Amant, Prairie Rose Goodwin, Ignacio Dominguez, David L. Roberts.  2015.  Toward Expert Typing in ACT-R. Proceedings of the 2015 International Conference on Cognitive Modeling (ICCM 15).
2013
Titus Barik, Arpan Chakraborty, Brent Harrison, David L. Roberts, Robert St. Amant.  2013.  Modeling the Concentration Game with ACT-R. The 12th International Conference on Cognitive Modeling.

This paper describes the development of subsymbolic ACT-R models for the Concentration game. Performance data is taken from an experiment in which participants played the game un- der two conditions: minimizing the number of mismatches/ turns during a game, and minimizing the time to complete a game. Conflict resolution and parameter tuning are used to implement an accuracy model and a speed model that capture the differences for the two conditions. Visual attention drives exploration of the game board in the models. Modeling re- sults are generally consistent with human performance, though some systematic differences can be seen. Modeling decisions, model limitations, and open issues are discussed.