Quantifying Loss Aversion in Cyber Adversaries via LLM Analysis
This work addresses the challenge of dynamically interpreting attacks in cybersecurity by providing a novel method to infer cognitive biases from hacker behavior, which is incremental as it builds on existing approaches to anticipate attacker strategies.
The paper tackled the problem of quantifying loss aversion in cyber adversaries by analyzing hacker behavior using large language models (LLMs), and the result demonstrated that LLMs can effectively dissect and interpret nuanced behavioral patterns to enhance cyber defense strategies.
Understanding and quantifying human cognitive biases from empirical data has long posed a formidable challenge, particularly in cybersecurity, where defending against unknown adversaries is paramount. Traditional cyber defense strategies have largely focused on fortification, while some approaches attempt to anticipate attacker strategies by mapping them to cognitive vulnerabilities, yet they fall short in dynamically interpreting attacks in progress. In recognition of this gap, IARPA's ReSCIND program seeks to infer, defend against, and even exploit attacker cognitive traits. In this paper, we present a novel methodology that leverages large language models (LLMs) to extract quantifiable insights into the cognitive bias of loss aversion from hacker behavior. Our data are collected from an experiment in which hackers were recruited to attack a controlled demonstration network. We process the hacker generated notes using LLMs using it to segment the various actions and correlate the actions to predefined persistence mechanisms used by hackers. By correlating the implementation of these mechanisms with various operational triggers, our analysis provides new insights into how loss aversion manifests in hacker decision-making. The results demonstrate that LLMs can effectively dissect and interpret nuanced behavioral patterns, thereby offering a transformative approach to enhancing cyber defense strategies through real-time, behavior-based analysis.