The residential environment is constantly evolving technologically. With this evolution, sensors have become intelligent interconnecting home appliances, personal computers, and mobile devices. Despite the benefits of this interaction, these devices are also prone to security threats and vulnerabilities. Ensuring the security of smart homes is challenging due to the heterogeneity of applications and protocols involved in this environment. This work proposes the FamilyGuard architecture to add a new layer of security and simplify management of the home environment by detecting network traffic anomalies. Experiments are carried out to validate the main components of the architecture. An anomaly detection module is also developed by using machine learning through one-class classifiers based on the network flow. The results show that the proposed solution can offer smart home users additional and personalized security features using low-cost devices.

We have seen significant growth in adopting new protocols and intelligent devices for the residential environment in recent years. These devices provide convenience, security, and energy efficiency to users. For instance, security cameras can detect unauthorized movement, and smoke sensors can detect potential fire accidents. However, many examples have shown that such devices may generate new cyber threats causing concerns about the security of sensitive data present in the home environment. In this article, we explore the application of machine learning to identify anomalous activities that can take place in a smart-home environment. We evaluated the performance of twelve one-class algorithms in three test cases; the best models showed an AUC higher than 94%, which indicates the usefulness of adopting unsupervised learning algorithms in identifying anomalous traffic in home networks.

O ensino de Computação tem se desenvolvido constantemente em resposta às inovações em tecnologia da informação. As Maratonas de Programação surgiram como uma das iniciativas para suprir a crescente demanda por profissionais com habilidades em raciocínio lógico-matemático. Com o intuito de contribuir para aprimorar o ensino de Computação, este estudo utilizou uma abordagem qualitativa, analisando as respostas de estudantes por meio de entrevistas. O objetivo deste trabalho é identificar padrões da rotina de participantes com destaque em competições de programação, a partir da experiência de ex-competidores, a fim de utilizar essas informações para propostas de melhoria dos modelos de ensino vigentes em trabalhos futuros. Os resultados destacam que a disciplina no estudo, com um planejamento de horários adequado e a prática em simuladores, como os sistemas de juízes online, são fundamentais para um aprendizado sólido e alinhado às demandas contemporâneas.

5G aims to provide a wireless access network that supports the requirements of different vertical markets such as automotive and smart cities. 5G oriented services use the Internet of Things (IoT) and will use the network programmability by integrating Software-defined Networking (NFV) and Network Function Virtualization (NFV). This context presents several multi-facet challenges to experiment with these services. 5GINFIRE is a 5G oriented experimental facility where different actors of the 5G ecosystem can experimentally contribute to 5G development and deployment. This work presents the 5GGINFIRE ecosystem, highlights some of its capabilities and some 5G ongoing experiments.

The network traffic classification allows improving the management, and the network services offer taking into account the kind of application. The future network architectures, mainly mobile networks, foresee intelligent mechanisms in their architectural frameworks to deliver application-aware network requirements. The potential of convolutional neural networks capabilities, widely exploited in several contexts, can be used in network traffic classification. Thus, it is necessary to develop methods based on the content of packets transforming it into a suitable input for CNN technologies. Hence, we implemented and evaluated the Packet Vision, a method capable of building images from packets raw-data, considering both header and payload. Our approach excels those found in state-of-the-art by delivering security and privacy by transforming the raw-data packet into images. Therefore, we built a dataset with four traffic classes evaluating the performance of three CNNs architectures: AlexNet, ResNet-18, and SqueezeNet. Experiments showcase the Packet Vision combined with CNNs applicability and suitability as a promising approach to deliver outstanding performance in classifying network traffic.