Game Theory and Machine Learning for Security
This tutorial features the recent advances in integrating game theory with machine learning to handle security challenges. It covers several frameworks of integration, including prediction based prescription, deep learning powered strategy generation, and differentiable learning of game parameters. It will also cover how these frameworks can be used to handle challenges in green security (protecting wildlife, forest, etc.) and cyber security domains.
Game theoretic frameworks have been successfully applied to solve real-world security problems in which security agencies (defenders) allocate limited resources to protect important targets against human adversaries, with a rich body of research publications at IJCAI and other AI venues. More recently, there is a rising interest in combining game theory and machine learning to get better defensive strategies in more complex security settings. We survey recent directions at the intersection of game theory and machine learning, with a focus on work aiming to address real-world security challenges such as environmental sustainability and cyber-security.
This tutorial will cover several frameworks of the integration of game theory and machine learning for security problems. After providing introductory material on game theory and machine learning, we will introduce the first framework – prediction based prescription. We will describe classical behavioral models of game players and how to learn such models from data. We will cover the latest work on predicting attacks from real-world data, and how to prescribe optimal defending strategy given the predictions. The second framework that will be introduced in the tutorial is deep learning powered strategy generation. We will introduce how to learn a good defender strategy for complex settings from simulated game plays using neural networks, and how the defender can learn to play when payoff information is not readily available. At the end, we will highlight work on differentiable learning of game parameters, followed by a discussion of opportunities for future work, including exciting new domains and fundamental theoretical and algorithmic challenges.
Fei Fang, Carnegie Mellon University
Fei Fang is an Assistant Professor at the Institute for Software Research at Carnegie Mellon University. Before joining CMU, she was a Postdoctoral Fellow at the Center for Research on Computation and Society (CRCS) at Harvard University. She received her Ph.D. from the Department of Computer Science at the University of Southern California in June 2016. She received her bachelor degree from the Department of Electronic Engineering, Tsinghua University in July 2011.
Her research lies in the field of artificial intelligence and multi-agent systems, focusing on data-aware game theory and mechanism design with applications to security, sustainability, and mobility domains. Her dissertation is selected as the runner-up for IFAAMAS-16 Victor Lesser Distinguished Dissertation Award, and is selected to be the winner of the William F. Ballhaus, Jr. Prize for Excellence in Graduate Engineering Research as well as the Best Dissertation Award in Computer Science at the University of Southern California. Her work has won the Innovative Application Award at Innovative Applications of Artificial Intelligence (IAAI’16), the Outstanding Paper Award in Computational Sustainability Track at the International Joint Conferences on Artificial Intelligence (IJCAI’15). Her work has been deployed by the US Coast Guard for protecting the Staten Island Ferry in New York City since April 2013. Her work has led to the deployment of PAWS (Protection Assistant for Wildlife Security) in multiple conservation areas around the world, which provides predictive and prescriptive analysis for anti-poaching effort.
- Motivations: Real-world security challenges
- Motivating domains: Port security, robot patrolling, wildlife protection, forest protection, cyber security etc.
- Goal: provide algorithms to make good (optimal) security decisions, including resource allocation and patrol scheduling
- Why game theory? Why machine learning?
- Background: Introduction to game theory and machine learning
- Nash equilibrium, Maximin and minimax strategies
- Leader-follower games, Stackelberg equilibrium
- Decision tree, graphical models, neural networks
- Framework 1: Prediction based prescription
- Building and learning human behavior models
- Predicting attacks from real-world data
- Planning defense based on predictions
- Framework 2: Deep learning powered strategy generation
- Policy learning for security games using neural networks
- Framework 3: Differentiable learning of game parameters
- Estimating game structure through end-to-end learning
- General Discussion/Questions
References and Additional Resources
- Policy Learning for Continuous Space Security Games using Neural Networks
Nitin Kamra, Umang Gupta, Fei Fang, Yan Liu, Milind Tambe
In AAAI-18: The Thirty-Second AAAI Conference on Artificial Intelligence, February 2018
- Optimal Patrol Planning for Green Security Games with Black-Box Attackers
Haifeng Xu, Benjamin Ford, Fei Fang, Bistra Dilkina, Andrew Plumptre, Milind Tambe, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Mustapha Nsubaga, Joshua Mabonga
In GameSec-17: The 8th Conference on Decision and Game Theory for Security
- Taking it for a Test Drive: A Hybrid Spatio-temporal Model for Wildlife Poaching Prediction Evaluated through a Controlled Field Test
Shahrzad Gholami, Benjamin Ford, Fei Fang, Andrew Plumptre, Milind Tambe, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Mustapha Nsubaga, Joshua Mabonga
In ECML-PKDD 2017: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
- PAWS – A Deployed Game-Theoretic Application to Combat Poaching
Fei Fang, Thanh H. Nguyen, Rob Pickles, Wai Y. Lam, Gopalasamy R. Clements, Bo An, Amandeep Singh, Brian C. Schwedock, Milind Tambe, Andrew Lemieux
AI Magazine, 38(1):23-36, 2017.
[pdf|AI Magazine version]