Instead, these limited resources must be allocated and scheduled efficiently, avoiding predictability, while simultaneously taking into account an adversary's response to the security coverage, the adversary's preferences and potential uncertainty over such preferences and capabilities. Computational game theory can help us build decision-aids for such efficient security resource allocation. Indeed, casting the security allocation problem as a Bayesian Stackelberg game, we have developed new algorithms that are deployed over multiple years in multiple applications:.
Fundamentally, we are focused on the research challenges in these efforts, marrying these applications with research on topics such as i fast algorithms for solving massive-scale games; ii behavioral game theory research for addressing human adversaries who may act with bounded rationality and imperfect observations; iii understanding the impact of players' limited observations on solution approaches adopted. We list the main research papers below and also some of our project application areas.
The objective of DARMS is to unify, quantify, and integrate information across the aviation sector in order to comprehensively assess risk on an individual, on a per flight basis. DARMS will integrate information on passengers, checked baggage and cargo, aircraft operators and airports and airport perimeters. Information - i. From the informational perspective, defense is all about shaping the attacker's belief regarding the protection of targets, and randomly allocating physical resources is just one way to achieve this.
The attacker's belief is also largely affected by the information available to him, such as payoff structures, effectiveness of physical resources, vulnerability of targets, defense deployments, etc. Crucially, the defender usually has more knowledge regarding these aspects than the attacker.
The central question we aim to answer in this project is, can the defender make use of such knowledge to increase the defensive effects, and if so, how she can do it optimally? Milind Tambe Keynote: Game theory for Security. Milind Tambe talks about Game Theory for Security. Milind Tambe provides an overview of security research projects.
Transportation Security Admininstration. Pittsburgh post-gazette. Skip to main content. Main Menu Utility Menu Search. Past Projects. Video Lectures on Security Games. Army Research Office. Click below to view TeamCore publications dating from through the present day. Tsai, T. Nguyen, N. Weller, M. Jiang, M. Jain, M. Delle Fave, M. Brown, C. Zhang, E. However, it is necessary to revise these approaches if there is a community of hackers with significant diversity in their behaviours.
In this paper, we introduce a novel approach to extend the basic ideas of applying game theory in stochastic modelling. The proposed method classifies the community of hackers based on two main criteria used widely in hacker classifications, which are motivation and skill. We use Markov chains to model the system and compute the transition rates between the states based on the preferences and the skill distributions of hacker classes.
The resulting Markov chains can be solved to obtain the desired security measures. We also present the results of an illustrative example using the proposed approach, which examines the relation between the attributes of the community of hackers and the security measures.
Tags: game theory , hacking. This one, like many, seem to suffer from the same basic flaw. I used my university access to see the full version of this paper. The flaw is that they propose some framework to model these cyber security problems, but because of the computational complexity of solving these problems, they only consider very simple examples. This would be ok if you could take the analysis of this simple problem and make some general statements that provide insight into the bigger problems.
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