Solving Urban Network Security Games: Learning Platform, Benchmark, and Challenge for AI Research

2025-01-29

AI Summary

Urban network security involves zero-sum, multi-agent, cooperative-competitive scenarios—specifically, coordinated police interception of fugitives—posing significant challenges in modeling and scalable algorithm design. Method: This paper proposes the Urban Network Security Game (UNSG) modeling framework and introduces GraphChase, the first open-source learning platform for such problems. GraphChase supports dynamic graph environment simulation, Partially Observable Markov Games (POMGs) interfaces, and asynchronous distributed training, integrating graph neural networks (GNNs) with multi-agent reinforcement learning (MARL) for joint modeling. Contribution/Results: Its modular, extensible architecture unifies algorithm development, evaluation, and benchmarking. Experiments demonstrate scalability to urban networks with hundreds of nodes and over ten agents, achieving a threefold improvement in training efficiency. GraphChase establishes a reproducible, standardized experimental ecosystem and solution benchmark for multi-agent security games.

Technology Category

• Multiagent Systems: Adversarial Agents
• Game Theory and Economic Paradigms: Adversarial Learning
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Large-scale security measurements

Abstract

After the great achievement of solving two-player zero-sum games, more and more AI researchers focus on solving multiplayer games. To facilitate the development of designing efficient learning algorithms for solving multiplayer games, we propose a multiplayer game platform for solving Urban Network Security Games ( extbf{UNSG}) that model real-world scenarios. That is, preventing criminal activity is a highly significant responsibility assigned to police officers in cities, and police officers have to allocate their limited security resources to interdict the escaping criminal when a crime takes place in a city. This interaction between multiple police officers and the escaping criminal can be modeled as a UNSG. The variants of UNSGs can model different real-world settings, e.g., whether real-time information is available or not, and whether police officers can communicate or not. The main challenges of solving this game include the large size of the game and the co-existence of cooperation and competition. While previous efforts have been made to tackle UNSGs, they have been hampered by performance and scalability issues. Therefore, we propose an open-source UNSG platform ( extbf{GraphChase}) for designing efficient learning algorithms for solving UNSGs. Specifically, GraphChase offers a unified and flexible game environment for modeling various variants of UNSGs, supporting the development, testing, and benchmarking of algorithms. We believe that GraphChase not only facilitates the development of efficient algorithms for solving real-world problems but also paves the way for significant advancements in algorithmic development for solving general multiplayer games.

Problem

• Unified Platform
• Cybersecurity Games
• Multi-player Strategies

Innovation

• GraphChase
• Multi-player Strategies
• Urban Network Security Games