The Industrial Internet of Things (IIoT) network is composed of devices that contain sensitive data, which makes them vulnerable to various security threats. Digital Certificates can be used to reinforce the security of the IIoT network, however, their management remains a major issue. Hence, in this article, we rely on trust management to deal with the whole certificate management process in IIoT networks, from revocation to verification. For this purpose, we organize the IIoT network into a clustering architecture where each cluster head (CH) hosts an agent, called CH-UR agent, that renews/revokes the certificates of its cluster member nodes. We apply signaling game theory to build a Certificate Revocation Game modeling the interactions between a member IIoT node and the CH-UR agent. Thus, upon the belief on the member node, updated by using the Bayesian rules, the best response strategy for the CH-UR agent can be obtained. Further, we propose a new efficient certificate verification scheme based on short-lived certificates (SLCs) and suitable for IIoT network requirements. The performance evaluation of our framework proves, first, the accuracy and convergence speed of our revocation mechanism to detect untrusted devices and on-off attacks. Second, the effectiveness of our clustering architecture to reduce the resource consumption resulting from the management of SLCs to 60% even with the increase of network density. Third, the effectiveness of the proposed certificate verification scheme to reduce the time needed to obtain the revocation information as well as the resulting storage and communication overhead to achieve this purpose.

Industrial IoT network (IIoT) is the result of integrating Internet of Things into the industrial processes to make manufacturers more efficient and reactive. This integration brings with it new security and privacy challenges because of the sensitive information exchanged between devices and the infrastructure. In order to prevent malicious nodes from abusing the IIoT system, this paper investigates a new distributed certificate revocation protocol. Hence, we propose cluster-based certificate revocation mechanism for IIoT network by using game theory. Indeed, our proposed signaling game allows to make a decision on a targeted node after evaluating its behaving. In this approach, we assume an IIoT network organized as a set of clusters called communities. Each community consists of a set of member nodes and a community leader who uses a multi-stage game to renew the certificate of well behaving member nodes and revoke the certificate of malicious member nodes. Based on the Perfect Bayesian Equilibrium, we describe a reactive algorithm implementing the certificates update mechanism. By simulations, we evaluate the convergence speed, the dynamic and the accuracy of the leader's posterior belief on nodes with different behaviors.

Automobile manufacturers around the world are increasingly deploying Industrial Internet of Things (IIoT) devices in their factories to accompany the Industrial Revolution 4.0. Security and privacy are the main limitations to the integration of Internet of Things (IoT) into industrial processes. Therefore, it is necessary to protect industrial data contained in IIoT devices and keep them confidential. As a step towards this direction, in this paper, we propose a dynamic trust management model suitable for industrial environments. We propose also to change the traditional centralized architecture of IIoT networks in automotive plants into a hybrid architecture based on a set of new industrial relationship rules. The performance evaluation in this work is done in two parts. In the first part, we compare our proposed architecture with the traditional architecture of the plant's IIoT network. The results of this comparison show that our architecture is more suitable to simplify trust management of IIoT devices. In the second part, we demonstrated the ability, the adaptiveness and the resiliency of our proposed trust model against behavioral changes of IIoT nodes in malicious environments.

In order to face the security risks and challenges imposed by the openness nature of the Internet of Things (IoT), this paper proposes a distributed advanced analytical trust model based on a Markov chain. The main objective of this paper is to study, improve and adapt a distributed trust model already proposed for Vehicular Ad hoc Networks (VANETs) to the IoT context. This adaptation uses specific parameters to the IoT networks and considers all limitations of IoT devices as well as the less mobile nature of these devices than vehicles. In this model, a set of neighbor nodes organize themselves into groups to evaluate and monitor another IoT node called monitored node depending on its behavior in the network. Our model uses an estimation algorithm to remove spams provided by malicious neighbors during the process of updating the monitored node trust. The numerical results illustrate the adaptability, the robustness and the strong resistance of our adapted trust model against the dynamic behaviors of the IoT devices and against the various popular attacks related to trust.