Abstract-Coupling cyber and physical systems gives rise to numerous engineering challenges and opportunities. An important challenge is the contagion of failure from one system to another, which can lead to large-scale cascading failures. However, the self-healing ability emerges as a valuable opportunity where the overlaying cyber network can cure failures in the underlying physical network. To capture both self-healing and contagion, this paper considers a graphical model representation of an interdependent cyber-physical system, in which nodes represent various cyber or physical functionalities, and edges capture the interactions between the nodes. A message-passing algorithm is proposed for this representation to study the dynamics of failure propagation and healing. By conducting a density evolution analysis for this algorithm, network reaction to initial disruptions is investigated. It is proved that as the number of message-passing iterations increases, the network reaches a steady-state condition that would be either a complete healing or a complete collapse. Then, a sufficient condition is derived to select the network parameters to guarantee the complete healing of the system. The result of the density evolution analysis is further employed to jointly optimize the design of cyber and physical networks for maximum resiliency. This analytical framework is then extended to the cases where propagation of failures in the physical network is faster than the healing responses of the cyber network. Such scenarios are of interest in many real-life applications such as smart grid. Finally, extensive numerical results are presented to verify the analysis and investigate the impact of the network parameters on the resiliency of the network.

Recent developments have made autonomous vehicles (AVs) closer to hitting our roads. However, their security is still a major concern among drivers as well as manufacturers. Although some work has been done to identify threats and possible solutions, a theoretical framework is needed to measure the security of AVs. In this paper, a simple security model based on defense graphs is proposed to quantitatively assess the likelihood of threats on components of an AV in the presence of available countermeasures. A Bayesian network (BN) analysis is then applied to obtain the associated security risk. In a case study, the model and the analysis are studied for GPS spoofing attacks to demonstrate the effectiveness of the proposed approach for a highly vulnerable component.

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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