Optimal and Near-Optimal Resource Allocation for Information Security and Critical Infrastructure Protection

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

This grant provides funding for the investigation of optimal and near-optimal strategies for defense of critical infrastructure from an intelligent and adaptable adversary. Game-theoretic optimization models will be developed taking into account the structure of the system to be defended (e.g., series and/or parallel subsystems), and also varying assumptions about the goals, knowledge, and constraints of both the defender and the adversary. Systems that are too complex to yield closed-form optimal results will be analyzing using a heuristic attack strategy that is demonstrably near-optimal for a wide range of cases, as a basis for developing optimal or near-optimal defenses against those attack strategies. If successful, the results of this research will lead to improved guidance for allocation of resources to critical infrastructure protection. The primary goal of this work is to identify defensive investment strategies that are optimized to defend against attackers with varying assumed goals, knowledge, and constraints. For example, attacker goals may include maximizing the probability that an attack succeeds, maximizing the damage caused by an attack if successful, or maximizing the expected value of the damage caused by an attack. Similarly, attackers may have differing levels of knowledge about the system's defensive investments, and differing constraints (e.g., limits on the number of failed attacks before the attacker is detected and disabled. Determining optimal or near-optimal strategies to defend against attackers with varying characteristics will help both to improve the security of our critical infrastructure, and also to reduce the cost of critical infrastructure protection. This work will also contribute to the computational tools and methodologies available for resource allocation problems in complex systems.