Multidefender Security Games

Current Stackelberg security game models primarily focus on isolated systems in which only one defender is present, despite being part of a more complex system with multiple players. However, many real systems such as transportation networks and the power grid exhibit interdependencies among targets and, consequently, between decision makers jointly charged with protecting them. To understand such multidefender strategic interactions present in security scenarios, the authors investigate security games with multiple defenders. Unlike most prior analyses, they focus on situations in which each defender must protect multiple targets, so even a single defender's best response decision is, in general, nontrivial. Considering interdependencies among targets, the authors develop a novel mixed-integer linear programming formulation to compute a defender's best response, and approximate Nash equilibria of the game using this formulation. Their analysis shows how network structure and the probability of failure spread determine the propensity of defenders to over- or underinvest in security.

Authors

• Jian Lou ZJU-Hangzhou Global Scientific and Technological Innovation Center
• Andrew M. Smith
• Yevgeniy Vorobeychik Washington University, St. Louis

Keywords

• Computer security
• Games
• Nash equilibrium
• Biological system modeling
• Computational modeling
• Analytical models
• Schedules
• Security Game
• Heuristic
• Target Value
• General Case
• Power Grid
• Best Response
• Real Networks
• Mixed-integer Programming
• Isolation System
• Mixed Strategy
• US Federal
• Mixed Integer Linear Programming
• Negative Externalities
• Positive Externalities
• Game Model
• Decentralized System
• Subset Of Targets
• Linear Programming Formulation
• Cascading Failures
• Coverage Probability
• Optimal Decision
• Attack Target
• Single Attack
• Preferential Attachment
• Security Investment
• Social Security
• Solution Concept
• Contagion Process
• Synthetic Networks
• network problems
• constrained optimization
• economics
• distributed artificial intelligence
• intelligent systems