Misbehavior detection in ephemeral networks: a local voting game in presence of uncertainty
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A. Behfarnia and A. Eslami, "Misbehavior Detection in Ephemeral Networks: A Local Voting Game in Presence of Uncertainty," in IEEE Access, vol. 7, pp. 184629-184642, 2019
Emerging short-lived (ephemeral) connections between wireless mobile devices have raised concerns over the security of ephemeral networks. An important security challenge in these networks is to identify misbehaving nodes, especially in places where a centrally managed station is absent. To tackle this problem, a local voting-based scheme (game) in which neighboring nodes quickly decide whether to discredit an accused (target) node in mobile networks has been introduced in the literature. However, nodes' beliefs and reactions significantly affect the outcome of target node identification in the collaboration. In this paper, a plain Bayesian game between a benign node and a target node in one stage of a local voting-based scheme is proposed in order to capture uncertainties of nodes for target node identification. In this context, the expected utilities (payoffs) of players in the game are defined according to uncertainties of nodes regarding their monitoring systems, the type of target node and participants, and the outcome of the cooperation. Meanwhile, incentives are offered in payoffs in order to promote cooperation in the network. To discourage nodes from abusing incentives, a variable-benefit approach that rewards each player according to the value of their contribution to the game is introduced. Then, possible equilibrium points between a benign node and a malicious node are derived using a pure-strategy Bayesian Nash equilibrium (BNE) and a mixed-strategy BNE, ensuring that no node is able to improve its payoffs by changing its strategy. Finally, the behavior of malicious and benign nodes is studied via simulations. Specifically, it is shown how the aforementioned uncertainties and the designed incentives impact the strategies of the players and, consequently, the correct target-node identification.
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