The sixth-generation (6G) network intends to revolutionize the healthcare sector. It will offer smart healthcare (s-health) treatments and allow efficient patient remote monitoring, exposing the high potential of 6G communication technology in telesurgery, epidemic, and pandemic. Healthcare relies on 6G communication technology, diminishing barriers as time and space. S-health applications require strict network requirements, for instance, 99.999% of service reliability and 1 ms of end-to-end latency. However, it is a challenging task to manage network resources and applications towards such performance requirements. Hence, significant attention focuses on performance management as a way of searching for efficient approaches to adjust and tune network resources to application needs, assisting in achieving the required performance levels. In the literature, performance management employs techniques such as resource allocation, resource reservation, traffic shaping, and traffic scheduling. However, they are dedicated to specific problems such as resource allocation for a particular device, ignoring the heterogeneity of network devices, and communication technology. Thus, this article presents PRIMUS, a performance management architecture that aims to meet the requirements of low-latency and high-reliability in an adaptive way for s-health applications. As network slicing is central to realizing the potential of 5G–6G networks, PRIMUS manages traffic through network slicing technologies. Unlike existing proposals, it supports device and service heterogeneity based on the autonomous knowledge of s-health applications. Emulation results in Mininet-WiFi show the feasibility of meeting the s-health application requirements in virtualized environments.
A grande escala da Internet das Coisas exige estruturas complexas capazes de suportar cenários experimentais com escala suficiente para avaliar eficientemente soluções de cibersegurança contra ataques DDoS baseados em botnets. Este trabalho descreve uma arquitetura para o MENTORED Testbed, um ambiente de experimentação criado sobre a Infraestrutura Definida por Software da Rede Nacional de Ensino e Pesquisa (IDS-RNP). O testbed cria experimentos com redes de longa distância, tecnologias de nuvem e com dispositivos sem fio inseridos nos servidores do IDS-RNP. O comportamento do testbed foi analisado por meio de um caso de uso que reproduz automaticamente capturas de rede em um cenário realístico para avaliar propostas de detecção de botnets.
A Internet das Coisas (IoT) conecta objetos à Internet para prestar serviços inovadores. Entretanto, a ocorrência de ataques traffic side-channel temporais ameaçam ferir o princípio de privacidade dos usuários IoT ao revelar informações privilegiadas sobre o seu comportamento. Este trabalho apresenta um Mecanismo de Defesa Contra Ataques Traffic Side-Channel Temporais na IoT. O mecanismo segue dois módulos, o de teste de vulnerabilidade e o de proteção de privacidade. O módulo de teste de vulnerabilidade identifica os vazamentos temporais side-channel e inicia o processo de defesa, diferente dos trabalhos prévios que apenas identificam as vulnerabilidades. O módulo de proteção de privacidade implementa três abordagens para mascarar o comportamento dos dispositivos em rede e ocultar os vazamentos temporais, diferentemente dos trabalhos da literatura focam em outros vazamentos como eletromagnetismo ou consumo de energia. Os resultados da avaliação de desemprenho conduzida em um cenário experimental mostram que a melhor abordagem reduz a acurácia de identificação dos dispositivos em até 63%.
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