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Toward Personalized Deceptive Signaling for Cyber Defense Using Cognitive Models.

Edward A Cranford1, Cleotilde Gonzalez2, Palvi Aggarwal2

  • 1Department of Psychology, Carnegie Mellon University.

Topics in Cognitive Science
|July 30, 2020
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Summary
This summary is machine-generated.

Human attackers in cybersecurity games are less rational than predicted, attacking more often. An instance-based learning model accurately predicts this behavior, leading to adaptive signaling for personalized cyber defenses.

Keywords:
ACT-RCognitive modelsCyber deceptionInstance-based learningKnowledge-tracingModel-tracingSignalingStackelberg security game

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Area of Science:

  • Cybersecurity
  • Cognitive Science
  • Game Theory

Background:

  • Current cybersecurity active defense strategies rely on game theory and deceptive signaling, assuming rational human behavior.
  • Empirical evidence suggests human attackers deviate significantly from perfect rationality in simulated insider attack scenarios.

Purpose of the Study:

  • To develop a cognitive model that accurately predicts human attacker behavior in cybersecurity games.
  • To propose an adaptive signaling method for enhanced cybersecurity defenses based on real-time cognitive modeling.

Main Methods:

  • An instance-based learning cognitive model was developed within the ACT-R framework.
  • Human participants played an online game simulating insider attacks to gather behavioral data.
  • The cognitive model was used to predict human performance and biases in the game.

Main Results:

  • The instance-based learning model accurately predicted human attacker frequency, which was higher than predicted by rational models.
  • Observed human behavior demonstrated significant deviations from perfect rationality in the simulated attack environment.
  • The proposed adaptive signaling method leverages the cognitive model for real-time tracing of individual attacker experience.

Conclusions:

  • Human behavior in cybersecurity contexts is not perfectly rational, necessitating more sophisticated predictive models.
  • Adaptive signaling, informed by cognitive models, offers a pathway to personalized and more effective cybersecurity defenses.
  • Understanding and modeling cognitive biases is crucial for improving the robustness of active defense strategies.