Abstract In this paper a new approach for Zakat Calculation Models (ZCM) metamodelling was proposed, using Model Driven Architecture (MDA) concepts. Also this paper was highlighted the three interesting contributions of our previous work in the context of the present paper especially for providing a better understanding of the proposal viz; the ZCM-based process called ZCMGProcess, the MOF-based metamodel called ZC2M, and the Metamodel-based... In this paper a new approach for Zakat Calculation Models (ZCM) metamodelling was proposed, using Model Driven Architecture (MDA) concepts. Also this paper was highlighted the three interesting contributions of our previous work in the context of the present paper especially for providing a better understanding of the proposal viz; the ZCM-based process called ZCMGProcess, the MOF-based metamodel called ZC2M, and the Metamodel-based tool called ZCMGenerator. This tool has been used to demonstrate the feasibility of the proposed approach and to facilitate its adoption. The approach adopted in this work was inspired on idea of Architecture Driven Modernization (ADM) and respects the MDA’s Four-Tier architecture. Read More Read Less

Abstract The evolution of the Intelligent Transport System (ITS) has imposed a major impact on road safety by providing a wide variety of applications to decrease the number of accidents and traffic management. As an emerging component of ITS, Vehicular Ad-hoc Network (VANET) is considered as a promising technology for increasing road safety. Basically, VANET provides two types of communications: vehicle-to-vehicle (V2V) and vehicle-infrastructure... The evolution of the Intelligent Transport System (ITS) has imposed a major impact on road safety by providing a wide variety of applications to decrease the number of accidents and traffic management. As an emerging component of ITS, Vehicular Ad-hoc Network (VANET) is considered as a promising technology for increasing road safety. Basically, VANET provides two types of communications: vehicle-to-vehicle (V2V) and vehicle-infrastructure (V2I) communications. While the vehicles act in a cooperative way to enhance road traffic safety from adopting the V2V communication, also known as Intervehicle communication (IVC), the biggest challenge is how to address the overhead and stability issues that are caused by the important number of messages generated by vehicles at critical areas, such as intersections. In this paper, we focus on the implementation of distributed system based on a passive data dissemination approach and dynamic clustering. This consist on each vehicle sending periodical measures about the position, velocity to other vehicles belonging to the same signal range and to the same cluster. Which implied an earlier divi-sion of the network into virtual subgroups to ease management and data dissemination of messages. Afterward, we simulate the solution using microsimulation taking in consideration the behavior of each vehicle. Read More Read Less

Abstract Attack detection is becoming one of the most active area in cybercrime detection processing. It’s importance coincidence with the growth of network application and electronic devices (computer, mo-bile phones, android, etc). Most of existing approaches of attack detection rely mainly on a nite set of attack. However, these solutions are vulnerable, that is, they fail in detecting some attacks when sources of informations... Attack detection is becoming one of the most active area in cybercrime detection processing. It’s importance coincidence with the growth of network application and electronic devices (computer, mo-bile phones, android, etc). Most of existing approaches of attack detection rely mainly on a nite set of attack. However, these solutions are vulnerable, that is, they fail in detecting some attacks when sources of informations are ambiguous or imperfect. But, few approaches started investigating toward this direction. Following this trends, this paper investigates the role of machine learning approach (ANN, SVM, clustering, etc) in detecting TCP connection traffic as normal or suspicious one. But, some classifiers, using random factors, can generate false, higher overall accuracy of detection. In this paper fusion of two classifiers has been proposed, where artificial neural network (ANN) classi er and support vector machine (SVM) were employed. Additionally, proposed solution allows to visualize obtained classification results. Accuracy of the pro-posed solution has been compared with other classifier results. Experiments have been conducted with different network connection selected from NSL-KDD DARPA dataset. Empirical results show that combining ANN and SVM techniques for attack detection is a promising direction. Read More Read Less