3,652 research outputs found
Decentralized Protection Strategies against SIS Epidemics in Networks
Defining an optimal protection strategy against viruses, spam propagation or
any other kind of contamination process is an important feature for designing
new networks and architectures. In this work, we consider decentralized optimal
protection strategies when a virus is propagating over a network through a SIS
epidemic process. We assume that each node in the network can fully protect
itself from infection at a constant cost, or the node can use recovery
software, once it is infected.
We model our system using a game theoretic framework and find pure, mixed
equilibria, and the Price of Anarchy (PoA) in several network topologies.
Further, we propose both a decentralized algorithm and an iterative procedure
to compute a pure equilibrium in the general case of a multiple communities
network. Finally, we evaluate the algorithms and give numerical illustrations
of all our results.Comment: accepted for publication in IEEE Transactions on Control of Network
System
Complete game-theoretic characterization of SIS epidemics protection strategies
International audienceDefining an optimal protection strategy against viruses, spam propagation or any other kind of contamination process is an important feature for designing new networks and architectures. In this work, we consider decentralized optimal protection strategies when a virus is propagating over a network through a Susceptible Infected Susceptible (SIS) epidemic process. We assume that each node in the network can fully protect itself from infection at a constant cost, or the node can use recovery software, once it is infected. We model our system using a game theoretic framework. Based on this model, we find pure and mixed equilibria, and evaluate the performance of the equilibria by finding the Price of Anarchy (PoA) in several network topologies. Finally, we give numerical illustrations of our results
Evolutionary Poisson Games for Controlling Large Population Behaviors
Emerging applications in engineering such as crowd-sourcing and
(mis)information propagation involve a large population of heterogeneous users
or agents in a complex network who strategically make dynamic decisions. In
this work, we establish an evolutionary Poisson game framework to capture the
random, dynamic and heterogeneous interactions of agents in a holistic fashion,
and design mechanisms to control their behaviors to achieve a system-wide
objective. We use the antivirus protection challenge in cyber security to
motivate the framework, where each user in the network can choose whether or
not to adopt the software. We introduce the notion of evolutionary Poisson
stable equilibrium for the game, and show its existence and uniqueness. Online
algorithms are developed using the techniques of stochastic approximation
coupled with the population dynamics, and they are shown to converge to the
optimal solution of the controller problem. Numerical examples are used to
illustrate and corroborate our results
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