Biblio

The e-government concept and healthcare have usually been studied separately. Even when and where both e-government and healthcare systems were combined in a study, the roles of e-government in healthcare have not been examined. As a result., the complementarity of the systems poses potential challenges. The interpretive approach was applied in this study. Existing materials in the areas of healthcare and e-government were used as data from a qualitative method viewpoint. Dimension of change from the perspective of the structuration theory was employed to guide the data analysis. From the analysis., six factors were found to be the main roles of e-government in the implementation and application of e-health in the delivering of healthcare services. An understanding of the roles of e-government promotes complementarity., which enhances the healthcare service delivery to the community.

Distributed Denial of Service (DDoS) attacks are intense and are targeted to major infrastructure, governments and military organizations in each country. There are a lot of mitigations about DDoS, and the concept of Content Delivery Network (CDN) has been able to avoid attacks on websites. However, since the existing CDN system is fundamentally centralized, it may be difficult to prevent DDoS. This paper describes the distributed CDN Schema using Private Blockchain which solves the problem of participation of existing transparent and unreliable nodes. This will explain DDoS mitigation that can be used by military and government agencies.

Current methods for producing and disseminating analytic products contribute to the latency of relaying actionable information and analysis to the U.S. Intelligence Community's (IC's) principal customers, U.S. policymakers and warfighters. To circumvent these methods, which can often serve as a bottleneck, we report on the results of a public prize challenge that explored the potential for artificial intelligence techniques to generate useful analytic products. The challenge tasked solvers to develop algorithms capable of searching and processing nearly 15,000 unstructured text files into a 1-2 page analytic product without human intervention; these analytic products were subsequently evaluated and scored using established IC methodologies and criteria. Experimental results from this challenge demonstrate the promise for the ma-chine generation of analytic products to ensure that the IC warns and informs in a more timely fashion.

Smart city is gaining a significant attention all around the world. Narrowband technologies would have strong impact on achieving the smart city promises to its citizens with its powerful and efficient spectrum. The expected diversity of applications, different data structures and high volume of connecting devices for smart cities increase the persistent need to apply narrowband technologies. However, narrowband technologies have recognized limitations regarding security which make them an attractive target to cyber-attacks. In this paper, a novel platform architecture to secure smart city against cyber attackers is presented. The framework is providing a threat deep learning-based model to detect attackers based on users data behavior. The proposed architecture could be considered as an attempt toward developing a universal model to identify and block Denial of Service (DoS) attackers in a real time for smart city applications.

The ability to discover patterns of interest in criminal networks can support and ease the investigation tasks by security and law enforcement agencies. By considering criminal networks as a special case of social networks, we can properly reuse most of the state-of-the-art techniques to discover patterns of interests, i.e., hidden and potential links. Nevertheless, in time-sensible scenarios, like the one involving criminal actions, the ability to discover patterns in a (near) real-time manner can be of primary importance.In this paper, we investigate the identification of patterns for link detection and prediction on an evolving criminal network. To extract valuable information as soon as data is generated, we exploit a stream processing approach. To this end, we also propose three new similarity social network metrics, specifically tailored for criminal link detection and prediction. Then, we develop a flexible data stream processing application relying on the Apache Flink framework; this solution allows us to deploy and evaluate the newly proposed metrics as well as the ones existing in literature. The experimental results show that the new metrics we propose can reach up to 83% accuracy in detection and 82% accuracy in prediction, resulting competitive with the state of the art metrics.