Impact of content storage and retrieval mechanisms on the performance of vehicular delay-tolerant networks

“Copyright © [2010] IEEE. Reprinted from 18th International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2010). ISBN: 978-1-4244-8663-2 . This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.”Vehicular Delay-Tolerant Networking (VDTN) is a new disruptive network architecture based on the concept of delay tolerant networks (DTNs). VDTNs handle non-real time applications using vehicles to carry messages on their buffers, relaying them only when a proper contact opportunity occurs. Therefore, the network performance is directly affected by the storage capacity and message retrieving of intermediate nodes. This paper proposes a suitable content storage and retrieval (CSR) mechanism for VDTN networks. This CSR solution adds additional information on control labels of the setup message associated to the corresponding data bundle (aggregated traffic) that defines and applies caching and forwarding restrictions on network traffic (data bundles). Furthermore, this work presents a performance analysis and evaluation of CSR mechanisms over a VDTN application scenario, using a VDTN testbed. This work presents the comparison of the network behavior and performance using two DTN routing protocols, Epidemic and Spray and Wait, with and without CSR mechanisms. The results show that CSR mechanisms improve the performance of VDTN networks significantly.Part of this work has been supported by the Instituto de Telecomunicações, Next Generation Networks and Applications Group (NetGNA), Portugal in the framework of the Project VDTN@Lab, and by the Euro-NF Network of Excellence from the Seventh Framework Programme of EU, in the framework of the Specific Joint Research Project VDTN

A GRU deep learning system against attacks in software defined networks

[EN] The management of modern network environments is becoming more and more complex due to new requirements of devices' heterogeneity regarding the popularization of the Internet of Things (IoT), as well as the dynamic traffic required by next-generation applications and services. To address this problem, Software-defined Networking (SDN) emerges as a management paradigm able to handle these problems through a centralized high-level network approach. However, this centralized characteristic also creates a critical failure spot since the central controller may be targeted by malicious users aiming to impair the network operation. This paper proposes an SDN defense system based on the analysis of single IP flow records, which uses the Gated Recurrent Units (GRU) deep learning method to detect DDoS and intrusion attacks. This direct flow inspection enables faster mitigation responses, minimizing the attack's impact over the SDN. The proposed model is tested against several different machine learning approaches over two public datasets, the CICDDoS 2019 and the CICIDS 2018. Furthermore, a lightweight mitigation approach is presented and evaluated through performance tests regarding each detection method. Finally, a feasibility test is performed regarding the throughput of flows per second that each detection method can analyze. This test is accomplished through the use of real IP Flow data collected at a large-scale network. The results point out promising detection rates and an elevated amount of analyzed flows per second, which makes GRU a feasible approach for the proposed system.This study has been partially supported by the National Council for Scientific and Technological Development (CNPq) of Brazil under Grant of Project 310668/2019-0; by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P; and by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES) by the granting of a scholarship through the "Programa de Doutorado Sanduiche no Exterior (PDSE) 2019". Finally, this work was supported by Federal University of Parana (UFPR) under Project Banpesq/2014016797.Assis, MV.; Carvalho, LF.; Lloret, J.; Proença Jr, ML. (2021). A GRU deep learning system against attacks in software defined networks. Journal of Network and Computer Applications. 177:1-13. https://doi.org/10.1016/j.jnca.2020.10294211317

AdaptWeb® - Evolução e Desafios

O AdaptWeb® (Ambiente de Ensino-Aprendizagem Adaptativo naWeb) é um Sistema Adaptativo de EAD baseado na web e tem a finalidade de adaptar o conteúdo, a apresentação e a navegação de acordo com o perfil do usuário. A sua adaptação é suportada pela criação de um modelo flexível do aluno, onde, para cada aluno, são armazenadas informações pessoais tais como seu curso, conhecimento, preferências e histórico navegacional, recursos tecnológicos e estilo de aprendizagem. Este trabalho apresenta a evolução do ambiente AdaptWeb® e os desafios para melhorar a interação do aluno com o ambiente e o processo de ensino-aprendizagem

A fuzzy ELECTRE structure methodology to assess big data maturity in healthcare SMEs

Advances in technology and an increase in the amount and complexity of data that are generated in healthcare have led to an indispensable revolution in this sector related to big data. Analytics of information based on multimodal clinical data sources requires big data projects. When starting big data projects in the healthcare sector, it is often necessary to assess the maturity of an organization with respect to big data, i.e., its capacity in managing big data. The assessment of the maturity of an organization requires multicriteria decision making as there is no single criterion or dimension that defines the maturity level regarding big data but an entire set of them. Based on the ISO 15504, this article proposes a fuzzy ELECTRE structure methodology to assess the maturity level of small- and medium-sized enterprises in the healthcare sector. The obtained experimental results provide evidence that this methodology helps to determine and compare maturity levels in big data management of organizations or the evolution of maturity over time. This is also useful in terms of diagnosing the readiness of an organization before starting to implement big data initiatives or technologies. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature

Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis

By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P 5 × 10 8. These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overla

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use