Computational Game Theory for Security and Sustainability

Abstract

Security is a critical concern around the world. In many domains from counter-terrorism to sustainability, limited security resources prevent full security coverage at all times; instead, these limited resources must be scheduled, while simultaneously taking into account different target priorities, the responses of the adversaries to the security posture and potential uncertainty over adversary types. Computational game theory can help design such security schedules. Indeed, casting the problem as a Bayesian Stackelberg game, we have developed new algorithms that are now deployed over multiple years in multiple applications for security scheduling. These applications are leading to real-world use-inspired research in the emerging research area of "security games"; specifically, the research challenges posed by these applications include scaling up security games to large-scale problems, handling significant adversarial uncertainty, dealing with bounded rationality of human adversaries, and other interdisciplinary challenges.

Deterrence against Terrorist Attacks in Sports‐Mega Events: A Method to Identify the Optimal Portfolio of Defensive Countermeasures

“…If they decided to attack, they then select a target and an attack modality. This situation fits precisely into a Stackelberg game, where the defense is the lead player and the attacker is the following player (Jiang, Jain, & Tambe, 2014). Building on research using this type of game to model such attacker-defender scenarios in security domains (e.g., see Pita et al, 2008;Shieh et al, 2012;Tsai et al, 2009), we model our problem as a Stackelberg game in which the defender moves first by allocating their available defensive resources and the attacker, after observing that move, decides if/how they are going to attack.…”

Cognitive Models in Cybersecurity: Learning From Expert Analysts and Predicting Attacker Behavior

“…Indeed, GT-based software has been successfully applied in real-world security contexts, including airport security, coast guard, police, and anti-poaching (animal preservation) efforts (Tambe et al, 2014), providing much-needed evidence that theory based on small toy problems scales to real-world asymmetric 12 security contexts. Real world security decision aids go beyond normative game theory (picking some optimal mix of actions assuming a perfectly rational opponent), and attempt to include attacker subjective utilities in the equation.…”

“…Game-theoretic approaches are useful for determining optimal mixes of strategies for leaving an attacker without a preferred strategy of their own. Moreover, GT-based defense algorithms have been successfully applied in many real-world security scenarios, including airport security, coast guard, police, and anti-poaching (animal preservation) efforts (Tambe et al, 2014). Veksler and Buchler (2016) and Cranford et al (2019) argue that cognitive modeling techniques, and more specifically model-tracing 1 and dynamic parameter fitting, may be used to track individual attacker preferences in real time, providing fairly high improvements over normative GT approaches in reducing the potential for successful attacks.…”

A Game Theoretic Approach for Digital Forensic Tool Selection †

“…In addition, it gives alert once the mixed-initiative overrides seem to degrade the desired randomization. With the similar physical security issues, Jiang et al [22] discuss challenges in security despite of limited resources. In this paper, authors focus on deployment and allocation of limited resources to prevent or reduce crimes in several large size domains such as airports, ports, forests, transit systems etc.…”