How can I quantify network resilience for AI-driven cyber defense?
Quantitative Resilience Modeling for Autonomous Cyber Defense
This paper proposes a method to measure the resilience of a computer network under cyberattack, particularly focusing on autonomous defense systems. It introduces a quantifiable resilience metric that considers different operational goals (confidentiality, availability, integrity), the criticality of various network resources, and the time evolution of attacks. This metric facilitates comparing different defense strategies, and insights from the metric are used to develop new reinforcement learning-based defensive agents that are more resilient than existing heuristic baselines.
Key points for LLM-based multi-agent systems: The concept of quantifying resilience is crucial for evaluating and improving LLM agents in complex, dynamic environments. The focus on temporal dynamics in evaluating resilience is relevant for assessing how LLMs adapt to evolving situations in multi-agent interactions. The use of reinforcement learning, albeit in a traditional game-theoretic setting, offers a potential avenue for training and enhancing the resilience of LLM-based agents, particularly when combined with novel methods for defining and evaluating their performance like the resilience metric proposed here. The ability to prioritize different operational goals (e.g., accuracy, safety, fairness) through weighted metrics provides a framework for aligning LLM behavior with human values in multi-agent settings.