Stackelberg Security Games (SSG) Basics and Application Overview

Abstract

Security is a critical concern around the world, whether it is the challenge of protecting ports, airports and other critical infrastructure, interdicting the illegal flow of drugs, weapons and money, protecting endangered species, forests and fisheries, suppressing urban crime or security in cyberspace. Unfortunately, limited security resources prevent full security coverage at all times; instead, we must optimize the use of limited security resources. To that end, we founded the "security games" framework to build decision-aids for security agencies. Security games is a novel area of research that is based on computational and behavioral game theory, while also incorporating elements of AI planning under uncertainty and machine learning. We have deployed securitygames based decision aids for infrastructure security such as at the ports and ferry traffic with the US coast guard (in the ports of New York, Boston, Los Angeles/Long Beach, Houston and others), for security of airports and air traffic with the US Federal Air Marshals and the Los Angeles World Airport (LAX) police, and tested this framework for security of metro trains with the Los Angeles Sheriff's Department. Moreover, recent work on "green security games" has led to testing our decision aids for protection of fisheries with the US Coast Guard and protection of wildlife at sites in multiple countries, and opportunistic crime security games have focused on suppressing urban crime. This chapter will discuss applications of security games, and outline research challenges in security games including algorithms for scaling up security games as well as for handling significant adversarial uncertainty and learning models of human adversary behaviors.

Reward-Based Deception with Cognitive Bias

“…The constraint in (20) computes the policy using the model in (14). We give the relationship between rewards and utilities in (21). Finally, (22) gives the total budget for utilities.…”

Dynamic Connectivity Game for Adversarial Internet of Battlefield Things Systems

“…To increase the uncertainty of its action and improve its payoff, the attacker will use a mixed strategy q t at each stage t, thus randomizing its choices across its pure strategies. The defender, on the other hand, responds with a pure strategy b t [17]. It is assumed that the defender can perfectly observe the strategy of the attacker at each stage t. The objective of the attacker is then to find the optimal mixed strategies q 1 , q 2 , ..., q T that maximize the sum of its expected payoffs until stage T max (15) where q at is the probabilitiy with which the attacker chooses action a t .…”

“Dice”-sion–Making Under Uncertainty: When Can a Random Decision Reduce Risk?

“…The Kantu' farmers in Borneo selected their farming sites through a ritualized form of birdwatching, the Naskapi hunters in Canada chose their hunting sites by reading the cracks on the scapula of a caribou, and the Azande people of central Africa took difficult decisions by poisoning a chicken and seeing whether it survived. 2 Public officials in ancient Athens were frequently chosen by lottery, and the nobles of Renaissance Venice selected their head of state through a partially randomized process so that no group could impose its will without an overwhelming majority or exceptional luck. More recently, British Columbia and Ontario have used randomly selected panels of Canadian citizens to propose changes in electoral regulations, and the International Skating Union adopted a random voting system in figure-skating competitions.…”