@conference {
	title = {Selfish response to Epidemic Propagation},
	author = {Theodorakopoulos, George., Le Boudec, Jean-Yves., Baras, John S.},
	year = {2011},
	month = {06/2011},
	abstract = {An epidemic spreading in a network calls for a decision on the part of the network members: They should decide whether to protect themselves or not. Their decision depends on the trade-off between their perceived risk of being infected and the cost of being protected. The network members can make decisions repeatedly, based on information that they receive about the changing infection level in the network. We study the equilibrium states reached by a network whose members increase (resp. decrease) their security deployment when learning that the network infection is higher (resp. lower). Our main result is that as the learning rate of the members increases, the equilibrium level of infection increases. We demonstrate this result both when members are strictly rational and when they are not. We characterize the domains of attraction of the equilibrium points.We validate our conclusions with simulations on human mobility traces.},
	address = {San Francisco, CA},
	booktitle = {2011 American Control Conference},
	pages = {pp. 4069-4074}
}
@conference {
	title = {Dynamic Network Security Deployment Under Partial Information},
	author = {Theodorakopoulos, George., Baras, John S., Le Boudec, Jean-Yves.},
	year = {2008},
	month = {09/2008},
	abstract = {A network user{\textquoteright}s decision to start and continue using security products is based on economic considerations. The cost of a security compromise (e.g., worm infection) is compared against the cost of deploying and maintaining a sufficient level of security. These costs are not necessarily the real ones, but rather the perceived costs, which depend on the amount of information available to a user at each time. Moreover, the costs (whether real or perceived) depend on the decisions of other users, too: The probability of a user getting infected depends on the security deployed by all the other users. In this paper, we combine an epidemic model for malware propagation in a network with a game theoretic model of the users{\textquoteright} decisions to deploy security or not. Users can dynamically change their decision in order to maximize their currently perceived utility. We study the equilibrium points~and their dependence on the speed of the learning process through which the users learn the state of the network. We find that the faster the learning process, the higher the total network cost.},
	address = {Monticello, IL},
	booktitle = {Forty-Sixth Annual Conference on Communication, Control and Computing},
	pages = {pp. 261-267}
}
@article {
	title = {Selfish response to Epidemic Propagation},
	author = {Theodorakopoulos, George., Le Boudec, Jean-Yves., Baras, John S.},
	year = {2013},
	month = {02/2013},
	abstract = {An epidemic that spreads in a network calls for a decision on the part of the network users. They have to decide whether to protect themselves or not. Their decision depends on the tradeoff between the perceived infection and the protection cost. Aiming to help users reach an informed decision, security advisories provide periodic information about the infection level in the network. We study the best-response dynamic in a network whose users repeatedly activate or de-activate security, depending on what they learn about the infection level. Our main result is the counterintuitive fact that the equilibrium level of infection increases as the users{\textquoteright} learning rate increases. The same is true when the users follow smooth best-response dynamics or any other continuous response function that implies a higher probability of protection when learning a higher level of infection. In both cases, we characterize the stability and the domains of attraction of the equilibrium points. Our finding also holds when the epidemic propagation is simulated on human contact traces, both when all users are of the same best-response behavior type and when they are of two distinct behavior types.},
	pages = {pp. 363 - 376},
	volume = {Volume 58.},
	number = {Issue 2},
	journal = {IEEE Transactions on Automatic Control.}
}
