Guarding Against Malicious Biased Threats (GAMBiT): Experimental Design of Cognitive Sensors and Triggers with Behavioral Impact Analysis

2025-11-26

AI Summary

Traditional cybersecurity defenses assume attackers are fully rational, overlooking their inherent cognitive constraints and biases. This paper proposes GAMBiT—the first proactive defense framework that explicitly models attacker cognitive states within adversarial game-theoretic interactions. GAMBiT embeds cognitive sensors and triggers to detect real-time deviations—including loss aversion, base-rate neglect, and sunk-cost fallacy—and dynamically intervenes in the attacker’s decision-making process. By shifting from passive protection to active cognitive manipulation, GAMBiT establishes the first systematic methodology for modeling and strategically exploiting psychological biases in cyber defense. Three controlled human-subject experiments (n = 61) demonstrate that GAMBiT significantly reduces attack task completion rates, induces substantial path deviation from optimal strategies, and increases behavioral detectability—collectively degrading attacker efficiency. The results validate the feasibility and efficacy of cognition-aware, game-theoretic active defense.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Game Theory and Economic Paradigms: Adversarial Learning
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Cyber-crime defenses and forensics
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

This paper introduces GAMBiT (Guarding Against Malicious Biased Threats), a cognitive-informed cyber defense framework that leverages deviations from human rationality as a new defensive surface. Conventional cyber defenses assume rational, utility-maximizing attackers, yet real-world adversaries exhibit cognitive constraints and biases that shape their interactions with complex digital systems. GAMBiT embeds insights from cognitive science into cyber environments through cognitive triggers, which activate biases such as loss aversion, base-rate neglect, and sunk-cost fallacy, and through newly developed cognitive sensors that infer attackers' cognitive states from behavioral and network data. Three rounds of human-subject experiments (total n=61) in a simulated small business network demonstrate that these manipulations significantly disrupt attacker performance, reducing mission progress, diverting actions off the true attack path, and increasing detectability. These results demonstrate that cognitive biases can be systematically triggered to degrade the attacker's efficiency and enhance the defender's advantage. GAMBiT establishes a new paradigm in which the attacker's mind becomes part of the battlefield and cognitive manipulation becomes a proactive vector for cyber defense.

Problem

• Introduces GAMBiT to exploit attacker cognitive biases for cyber defense
• Uses cognitive triggers and sensors to disrupt attacker performance and detectability
• Establishes cognitive manipulation as a new proactive cyber defense paradigm

Innovation

• Leverages cognitive biases as a new defensive surface
• Uses cognitive triggers to activate attacker biases
• Employs cognitive sensors to infer attacker cognitive states