Sofia Hirschmann

Quantifying Loss Aversion in Cyber Adversaries via LLM Analysis

Understanding and quantifying human cognitive biases from empirical data has long posed a formidable challenge, particularly in cybersecurity, where defending against unknown adversaries is...

Towards a virtual human improv troupe for social skills training

The Relationship Between Tracking Performance and Video Quality

Detecting Ambiguity Aversion in Cyberattack Behavior to Inform Cognitive Defense Strategies

Adversaries (hackers) attempting to infiltrate networks frequently face uncertainty in their operational environments. This research explores the ability to model and detect when they exhibit...

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

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....

Risk Psychology & Cyber-Attack Tactics

We examine whether measured cognitive processes predict cyber-attack behavior. We analyzed data that included psychometric scale responses and labeled attack behaviors from cybersecurity...