G-DPS: A Game-Theoretical Decision-Making Framework for Physical Surveillance Games

Critical infrastructure protection becomes increasingly a major concern in governments and industries. Besides the increasing rates of cyber-crime, recent terrorist attacks bring critical infrastructure into a severer environment. Many critical infrastructures, in particular those operating large industry complexes, incorporate some kind of physical surveillance technologies to secure their premises. Surveillance systems, such as access control and malicious behavior detection, have been long used for perimeter security as a first line of defense. Traditional perimeter security solutions typically monitor the outer boundary structures and lines, thus ignoring threats from the inside. Moreover, the deterrent effect of surveillance systems like Closed Circuit Television (CCTV) becomes considerably less important due to the inflexibility induced by their fixed installations. Hence, an infrastructure’s surveillance policy is more predictable and a potential adversary has a better opportunity to observe and bypass it subsequently. Therefore, it is important to maintain situational awareness within such environments so that potential intruders can still be detected. Regardless of whether personnel (e.g., security guards, etc.) or technical solutions (e.g., cameras, etc.) are applied, such surveillance systems have an imperfect detection rate, leaving an intruder with the potential to cause some damage to the infrastructure. Hence, the core problem is to find an optimal application of the surveillance technology at hand to minimize such a potential damage. This problem already has a natural reflection in game theory known as cops-and-robbers game but current models always assume a deterministic outcome of the gameplay. In this work, we present a decision-making framework, which assesses possible choices and alternatives towards finding an optimal surveillance configurations and hence minimizing addressed risks. The decision is made by means of a game-theoretic model for optimizing physical surveillance systems and minimizing the potential damage caused by an intruder with respect to the imperfect detection rates of surveillance technology. With our approach, we have the advantage of using categorical (or continuous) distributions instead of a single numerical value to capture the uncertainty in describing the potential damage of an intruder. This gives us the opportunity to model the imperfection of surveillance systems and to optimize over large collections of empirical or simulated data without losing valuable information during the process.