9 papers
Detecting Ambiguity Aversion in Cyberattack Behavior to Inform Cognitive Defense Strategies
Stephan Carney, Soham Hans, Sofia Hirschmann +4
Adversaries (hackers) attempting to infiltrate networks frequently face uncertainty in their operational environments. This research explores the ability to model and detect when t…
Guarding Against Malicious Biased Threats (GAMBiT): Experimental Design of Cognitive Sensors and Triggers with Behavioral Impact Analysis
Brandon Beltz, Po-Yu Chen, James Doty +16
This paper introduces GAMBiT (Guarding Against Malicious Biased Threats), a cognitive-informed cyber defense framework that leverages deviations from human rationality as a new def…
Security Logs to ATT&CK Insights: Leveraging LLMs for High-Level Threat Understanding and Cognitive Trait Inference
Soham Hans, Stacy Marsella, Sophia Hirschmann +1
Understanding adversarial behavior in cybersecurity has traditionally relied on high-level intelligence reports and manual interpretation of attack chains. However, real-time defen…
Risk Psychology & Cyber-Attack Tactics
Rubens Kim, Stephan Carney, Yvonne Fonken +5
We examine whether measured cognitive processes predict cyber-attack behavior. We analyzed data that included psychometric scale responses and labeled attack behaviors from cyberse…
Guarding Against Malicious Biased Threats (GAMBiT) Experiments: Revealing Cognitive Bias in Human-Subjects Red-Team Cyber Range Operations
Brandon Beltz, Jim Doty, Yvonne Fonken +9
We present three large-scale human-subjects red-team cyber range datasets from the Guarding Against Malicious Biased Threats (GAMBiT) project. Across Experiments 1-3 (July 2024-Mar…
Quantifying Loss Aversion in Cyber Adversaries via LLM Analysis
Soham Hans, Nikolos Gurney, Stacy Marsella +1
Understanding and quantifying human cognitive biases from empirical data has long posed a formidable challenge, particularly in cybersecurity, where defending against unknown adver…