Interdependent Security and Defense Games
Interdependent Security and Defense Games: From Airline Security to Vaccination and Cyber Defense
Attacks carried out by hackers and terrorists over the last few
years have led to increased efforts by both government and the private
sector to create and adopt mechanisms to prevent future attacks. This
effort has yielded a more focused research attention to models,
computational and otherwise, that facilitate and help to improve
(homeland) security for both physical infrastructure and
cyberspace. In particular, there has been quite a bit of recent
research activity in the general area of game-theoretic models for
The main objective of our project is to design models and
algorithms to analyze networked multi-agent system's situations in
which each agent is a node/vertex in some network being the source of
a potentially deliberate attack by an external malicious agent. The internal network agent is an independent decision-maker assessing the cost-effectiveness of investments in defense or security that eliminates the effectiveness of direct-attack but can only diminish the effectiveness of catching indirect attacks via potential transfer from unprotected neighbors.
We proposed interdependent defense (IDD) games, a computational game-theoretic framework to study aspects of the interdependence of risk and security in multi-agent systems under deliberate external attacks. Our model builds upon Interdependent security (IDS) games. IDS
applications include airline and port security, vaccination and cyber
defense. IDS games consider the source of the risk to be the result of
a fixed randomized-strategy.
We study computational questions
in a modified version of the IDS game in which investing in security
comes with some level of protection
from transfers. More importantly, we adapted IDS games to model the attacker's deliberate behavior. It is important to note that the explicit modeling of risk transfer is an aspect of our model that has not been the focus of previous game-theoretic attacker-defender models of security.
- Hau Chan (Ph.D. candidate; Successfully defended his
dissertation on June 5th, 2015)
- Luis E. Ortiz
- Michael Ceyko (former undergraduate; went on to M.S. Program in
CS at Harvard)
Relevant Publications and Extended Abstracts
- Hau Chan and Luis E. Ortiz. Learning Game Parameters from MSNE: An Application to Learning IDS Games.
To appear at The 26th International Conference on Game
Theory, part of the Stony Brook Game Theory Summer Festival
2015, July 2015.
- Hau Chan and Luis E. Ortiz. Computing Nash Equilibrium in
Interdependent Defense Games. In AAAI Conference on Artificial Intelligence,
- Hau Chan and Luis E. Ortiz. Computing Nash Equilibria in Generalized
Interdependent Security Games. In, Advances in
Neural Information Processing Systems (NIPS) 27, Z. Ghahramani,
M. Welling, C. Cortes, N.D. Lawrence, and K.Q. Weinberger,
Editors, Pages 2735--2743, 2014. Curran Associates, Inc.
Also presented as invited spotlight and poster at Workshop on Transactional Machine Learning and E-Commerce, Neural Information Processing Systems (NIPS) Dec. 12, 2014, Montreal, Quebec, Canada
- Hau Chan, Michael Ceyko, and Luis E. Ortiz. Interdependent
Defense Games: Modeling Interdependent Security under Deliberate
Attacks. In Proceedings of the
28th Conference on Uncertainty in Artificial Intelligence (UAI),
A previous related version:
- Michael Ceyko, Hau Chan, and Luis E. Ortiz. Interdependent
Defense Games: Modeling Interdependent Security under Deliberate Attacks
(Extended Abstract). In International Conference on Game
Theory, 22nd Stony Brook Game Theory Festival of the Game Theory Society, July 2011.
Currently only available upon request
- Ruby source code for generating random Internet games and
solving them using simple smoothed-best-response dynamics
Currently only available upon request
- DIMES Autonomous-systems-level Internet network graph for
Hau Chan was sponsored in large part by an NSF Graduate Research
Fellowship (GRFP) Award. Luis E. Ortiz is partially sponsored by NSF.
National Science Foundation