Misbehavior Detection in Ephemeral Networks: A Local Voting Game in Presence of Uncertainty

Abstract

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.

SGM: Strategic Game Model for Resisting Node Misbehaviour in IoT-Cloud Ecosystem

“…The study introduced an authentication mechanism to mitigate node misbehaviour using a collaborative approach of diffusing information about the local selfish node. Behfarnia and Eslami [29] developed a voting game-based scheme to identify node misbehaviour. The technique implements a single-stage Bayesian game for capturing information related to node uncertainties.…”

Game Theoretic Approaches in Vehicular Networks: A Survey

“…The weakness of this method is that it only focuses on detecting the false alert's generation attack but does not consider other types of attacks facing the VNs. [37], [38] [39], [40] [41]- [46] [47]- [50] [45], [46], [51], [52] [53]- [59] [60], [61] [62], [63] Stackelberg [64], [65] [66]- [69] [70]- [73] Network slicing [74] Bayesian General [75], [76] [ Sedjelmaci et al [35] further developed a lightweight hybrid IDS for VNs considering both the mobility of vehicles and various types of attacks, neither of which were considered in their previous work [75]. Three intrusion detection agents are considered in the hybrid IDS framework: Local Intrusion Detection System (LIDS) at the cluster member level, Global Intrusion Detection System (GIDS) at the CH level, and Global Decision System (GDS) at RSU level.…”

“…By adopting the monitoring strategies based on the NE, the volume of IDS traffic is significantly reduced while maintaining a satisfied detection rate and accuracy. The cyber defense game [64], [65] for VNs takes the works in [36] and [75] a step further by using a hierarchical Relate the target vehicles old pseudonym to its new pseudonym [132] Defender and attacker [76] with a case study of VNs. A voting-based Bayesian game between a benign node PLB and a target node PLT is proposed.…”