Biblio
The Internet of Vehicles (IoV) will connect not only mobile devices with vehicles, but it will also connect vehicles with each other, and with smart offices, buildings, homes, theaters, shopping malls, and cities. The IoV facilitates optimal and reliable communication services to connected vehicles in smart cities. The backbone of connected vehicles communication is the critical V2X infrastructures deployment. The spectrum utilization depends on the demand by the end users and the development of infrastructure that includes efficient automation techniques together with the Internet of Things (IoT). The infrastructure enables us to build smart environments for spectrum utilization, which we refer to as Smart Spectrum Utilization (SSU). This paper presents an integrated system consisting of SSU with IoV. However, the tasks of securing IoV and protecting it from cyber attacks present considerable challenges. This paper introduces an IoV security system using deep learning approach to develop secure applications and reliable services. Deep learning composed of unsupervised learning and supervised learning, could optimize the IoV security system. The deep learning methodology is applied to monitor security threats. Results from simulations show that the monitoring accuracy of the proposed security system is superior to that of the traditional system.
The rapid growth of population and industrialization has given rise to the way for the use of technologies like the Internet of Things (IoT). Innovations in Information and Communication Technologies (ICT) carries with it many challenges to our privacy's expectations and security. In Smart environments there are uses of security devices and smart appliances, sensors and energy meters. New requirements in security and privacy are driven by the massive growth of devices numbers that are connected to IoT which increases concerns in security and privacy. The most ubiquitous threats to the security of the smart grids (SG) ascended from infrastructural physical damages, destroying data, malwares, DoS, and intrusions. Intrusion detection comprehends illegitimate access to information and attacks which creates physical disruption in the availability of servers. This work proposes an intrusion detection system using data mining techniques for intrusion detection in smart grid environment. The results showed that the proposed random forest method with a total classification accuracy of 98.94 %, F-measure of 0.989, area under the ROC curve (AUC) of 0.999, and kappa value of 0.9865 outperforms over other classification methods. In addition, the feasibility of our method has been successfully demonstrated by comparing other classification techniques such as ANN, k-NN, SVM and Rotation Forest.
Today, we witness the emergence of smart environments, where devices are able to connect independently without human- intervention. Mobile ad hoc networks are an example of smart environments that are widely deployed in public spaces. They offer great services and features compared with wired systems. However, these networks are more sensitive to malicious attacks because of the lack of infrastructure and the self-organizing nature of devices. Thus, communication between nodes is much more exposed to various security risks, than other networks. In this paper, we will present a synthetic study on security concept for MANETs, and then we will introduce a contribution based on evaluating link quality, using ETX metric, to enhance network availability.
Today, we witness the emergence of smart environments, where devices are able to connect independently without human- intervention. Mobile ad hoc networks are an example of smart environments that are widely deployed in public spaces. They offer great services and features compared with wired systems. However, these networks are more sensitive to malicious attacks because of the lack of infrastructure and the self-organizing nature of devices. Thus, communication between nodes is much more exposed to various security risks, than other networks. In this paper, we will present a synthetic study on security concept for MANETs, and then we will introduce a contribution based on evaluating link quality, using ETX metric, to enhance network availability.
Today's control systems such as smart environments have the ability to adapt to their environment in order to achieve a set of objectives (e.g., comfort, security and energy savings). This is done by changing their behaviour upon the occurrence of specific events. Building such a system requires to design and implement autonomic loops that collect events and measurements, make decisions and execute the corresponding actions.The design and the implementation of such loops are made difficult by several factors: the complexity of systems with multiple objectives, the risk of conflicting decisions between multiple loops, the inconsistencies that can result from communication errors and hardware failures and the heterogeneity of the devices.In this paper, we propose a design framework for reliable and self-adaptive systems, where multiple autonomic loops can be composed into complex managers, and we consider its application to smart environments. We build upon the proposed framework a generic autonomic loop which combines an automata-based controller that makes correct and coherent decisions, a transactional execution mechanism that avoids inconsistencies, and an abstraction layer that hides the heterogeneity of the devices.We propose patterns for composition of such loops, in parallel, coordinated, and hierarchically, with benefits from the leveraging of automata-based modular constructs, that provides for guarantees on the correct behaviour of the controlled system. We implement our framework with the transactional middleware LINC, the reactive language Heptagon/BZR and the abstraction framework PUTUTU. A case study in the field of building automation is presented to illustrate the proposed framework.