Abstract

Abstract

Abstract
The availability of public services through online platforms has improved the coverage and efficiency of essential services provided to citizens worldwide. These services also promote transparency and foster citizen participation in government processes. However, the increased online presence also exposes sensitive data exchanged between citizens and service providers to a wider range of security threats. Therefore, ensuring the security and trustworthiness of online services is crucial to Electronic Government (EGOV) initiatives' success. Hence, this work assesses the security posture of online platforms hosted in 3068 governmental domain names, across all UN Member States, in three dimensions: support for secure communication protocols; the trustworthiness of their digital certificate chains; and services' exposure to known vulnerabilities. The results indicate that despite its rapid development, the public sector still falls short in adopting international standards and best security practices in services and infrastructure management. This reality poses significant risks to citizens and services across all regions and income levels.

Abstract
The increasing use of microservices architectures has been accompanied by the profusion of tools for their design and operation. One relevant tool is API Gateways, which work as a proxy for microservices, hiding their internal APIs, providing load balancing, and multiple encoding support. Particularly in cloud environments, where the inherent flexibility allows on-demand resource deployment, API Gateways play a key role in seeking quality of service. Although multiple solutions are currently available, a comparative performance assessment under real workloads to support selecting the more suitable one for a specific service is time-consuming. In this way, the present work introduces AGE, a service capable of automatically deploying multiple API Gateways scenarios and providing a simple comparative performance indicator for a defined workload and infrastructure. The designed proof of concept shows that AGE can speed up API Gateway deployment and testing in multiple environments. © 2023 IEEE.

Abstract
Sampling techniques play a key role in achieving efficient network measurements by reducing the amount of traffic processed while trying to maintain the accuracy of network statistical behavior estimation. Despite the evolution of current techniques regarding the correctness of network parameters estimation, the overhead associated with the volume of data involved in the sampling process is still considerable. In this context, this paper proposes a new technique for multiadaptive traffic sampling based on linear prediction, which allows to reduce significantly the traffic under analysis, keeping the representativeness of samples in capturing network behavior. A proof-of-concept, evaluating this technique for real traffic traces representing distinct traffic profiles, demonstrates the effectiveness of the proposal, outperforming classic techniques both in accuracy and data volumes processed. © 2012 Springer-Verlag.

Abstract
Traffic sampling techniques are crucial and extensively used to assist network management tasks. Nevertheless, combining accurate network parameters' estimation and flexible lightweight measurements is an open challenge. In this context, this paper proposes a self-adaptive sampling technique, based on linear prediction, which allows to reduce significantly the measurement overhead, while assuring that sampled traffic reflects the statistical characteristics of the global traffic under analysis. The technique is multiadaptive as several parameters are considered in the dynamic configuration of the traffic selection process. The devised test scenarios aim at exploring the proposed sampling technique ability to join accurate network estimates to reduced overhead, using throughput as reference parameter. The evaluation results, obtained resorting to real traffic traces representing wired and wireless aggregated traffic scenarios and actual network services, prove that the simplicity, flexibility and self-adaptability of this technique can be successfully explored to improve network measurements efficiency over distinct traffic conditions. For optimization purposes, this paper also includes a study of the impact of varying the order of prediction, i.e., of considering different degrees of past memory in the self-adaptive estimation mechanism. The significance of the obtained results is demonstrated through statistical benchmarking.