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On Mitigation Inefficiency of Selfish Investment in Network Recovery from High Loss SIS Infection
Published
Author(s)
Vladimir V. Marbukh
Abstract
This paper reports on quantification and mitigation of the inefficiency of selfish investment in network recovery from Susceptible Infected Susceptible (SIS) infection in a practically important case of high losses due to infection. In this case, both socially optimal and selfish investments in the infection loss mitigation keep the system close to the boundary of the infection-free region. However, our analysis reveals that while socially optimal investments result in asymptotically zero infection losses, this is not the case for selfish investments. The inefficiency of selfish investments, which is measured by the corresponding Price of Anarchy (PoA), is due to positive externalities. In heterogeneous networks, positive externalities result in finite infection losses despite aggregate overinvestment due to imbalances of selfish investments. While the infection losses can be eliminated with small increase in the selfish investments, dealing with imbalances of selfish investments is more challenging. This assessment challenges conventional view that inefficiency of selfish investment in network security is due to aggregate underinvestment, at least in a practically important case of large infection losses. We discuss possible approaches to reduction of the second inefficiency component through regulations, incentives, or their combination, and outline directions of future research.
Proceedings Title
International Workshop on Cyber-Physical Systems and Applications in conjunction with The 17th
IEEE International Conference On Trust, Security And Privacy In Computing And Communications
(IEEE TrustCom-18)
Marbukh, V.
(2018),
On Mitigation Inefficiency of Selfish Investment in Network Recovery from High Loss SIS Infection, International Workshop on Cyber-Physical Systems and Applications in conjunction with The 17th
IEEE International Conference On Trust, Security And Privacy In Computing And Communications
(IEEE TrustCom-18), New York, NY, [online], https://doi.org/10.1109/TrustCom/BigDataSE.2018.00293
(Accessed December 4, 2024)