Securing vehicular cloud networks

Vehicular Cloud Networks (VCN) is the network that ensures mobility and availability of resources allowing new services and applications like Network as a Service (NaaS), STorage as a Service (STaaS), Computation as a Service (CompaaS) and Cooperation as a Service (CaaS). In this paper, we propose a solution to secure the Vehicular Cloud Network (VCN). Our challenge in this work is to adapt the PKI architecture, which is mainly used in wired networks to be used in VCN. To propose a security solution for Vehicular Cloud Networks (VCN), our work is based on three steps; the first one is to make network architecture study, where we tried to highlight the main network components. The second step is to propose the security solution architecture. Finally, the last step is to program a test and validate the solution using a simulation

Performance evaluation of communications in distributed systems and web based service architectures

Performance evaluation is still a topic that attains a lot of attention in both distributed and mobile systems as well as web based services architectures. Due to the recent advances in internet based applications as well as distributed and mobile communication systems, we are witnessing a variety of new technologies. However,these systems are becoming very large and complex at the same time. Several challenges remain to be resolved before these systems become a commodity. Guaranteeing Quality of Service (QoS)and provisioning of web-based systems as well as distributed and mobile systems and evaluating their communication performance represent among the challenging problem in the design of these systems. Quantitative analysis can be very difficult and may be intractable because of the state space explosion. New methods and tools have recently emerged for these kinds of complex systems,such as Stochastic Automata Networks, Stochastic bounds, and so forth

A Copula-Based Attack Prediction Model for Vehicle-to-Grid Networks

The Vehicle-to-Grid (V2G) networks are a part of the Smart Grid networks. Their primary goal is to recharge electric vehicles. These networks, as with any computer system, are facing cyber attacks. For example, during a charge or recharge process, V2G networks can be vulnerable to attacks such as Man-in-the-Middle (MitM), Denial of Service (DoS), identity theft, and rebound attacks. It is therefore up to us to offer innovative solutions in order to reduce threats as much as possible. In this paper, a model based on copulas to detect intrusion cases in V2G networks is proposed. To achieve this model, a database is generated first from three scenarios using tools including MiniV2G, Wireshark, and CICflowMeter. Then, significant variables are selected using Principal Component Analysis (PCA). The classification algorithm is based on the notion of copulas constructed under the software R. From the obtained results, it emerges that the created model has a very high prediction rate of attacks in the aforementioned network

A Copula-Based Attack Prediction Model for Vehicle-to-Grid Networks

The Vehicle-to-Grid (V2G) networks are a part of the Smart Grid networks. Their primary goal is to recharge electric vehicles. These networks, as with any computer system, are facing cyber attacks. For example, during a charge or recharge process, V2G networks can be vulnerable to attacks such as Man-in-the-Middle (MitM), Denial of Service (DoS), identity theft, and rebound attacks. It is therefore up to us to offer innovative solutions in order to reduce threats as much as possible. In this paper, a model based on copulas to detect intrusion cases in V2G networks is proposed. To achieve this model, a database is generated first from three scenarios using tools including MiniV2G, Wireshark, and CICflowMeter. Then, significant variables are selected using Principal Component Analysis (PCA). The classification algorithm is based on the notion of copulas constructed under the software R. From the obtained results, it emerges that the created model has a very high prediction rate of attacks in the aforementioned network