Cybersecurity has become a global priority, and Brazil faces increasing challenges in the public sector due to cyberattacks that threaten sensitive data and national security. The digitalization of government services in Brazil has increased vulnerabilities, exacerbated by the shortage of specialized professionals and insufficient technological infrastructure. Strengthening cybersecurity in government agencies, such as the Secretariat of Planning and Management of the Government of the State of Ceará (SEPLAG-CE), is crucial to ensuring the continuity of services and protecting the privacy of citizens. In this context, the overall objective of the project is to improve SEPLAG-CE's cybersecurity posture through actions aimed at protecting its assets, data, services, and applications. To achieve this objective, the project intends to conduct intrusion tests to identify and correct vulnerabilities, test the security of SEPLAG-CE's applications, implement a vulnerability management process with risk analysis, implement procedures to reduce the incidence of phishing attacks, and develop an ongoing action plan to maintain cybersecurity. The methodology to be adopted involves collaborative action between the Red Team (which simulates attacks) and the Blue Team (which defends the environment), following a strategy of continuous feedback and improvement of organizational security.

The consolidation of the Internet into a global and pervasive communication network has led to the emergence of a connected global society that generates a massive amount of data. The Internet is, in reality, an interconnection of heterogeneous networks, flexible enough to allow the implementation of new applications. On the other hand, the dissemination of sensors in the most varied devices used in everyday life allows the construction of intelligent systems, such as smart cities, which have the potential to provide greater social well-being, as well as foster the economy. The emergence of Artificial Intelligence (AI) as a fundamental area for solving difficult problems has led to its adoption in different systems, such as the Internet of Things (IoT) and communication networks. The processing of raw data generated by sensors has the potential to generate information, knowledge and added value. The multiplicity and diversity of information in the operation of communication networks demands possible solutions derived only by the use of AI. This proposal aims to explore the introduction of intelligence in future communication networks and IoT, so that there are networks that enable new services capable of responding to the complex demands of society. The aim is to adopt a methodology for developing intelligent technologies inherent to the functioning of these networks and their services. Furthermore, the generation of specialized critical mass will enable the Brazilian State to remain at the forefront of the new digital era and its great opportunities for generating wealth and well-being.

The SMART NEtworks and ServiceS for 2030 (SMARTNESS) FAPESP Engineering Research Center (ERC) aims to advance cutting-edge research in computer networks and digital application services focused on strategic areas where scientific and technological impacts can be achieved by the year 2030, in collaboration with research communities of cloud and networking. With the deployment of 5G and the 6G vision being developed, the main challenges for SMARTNESS are how to design and operate cloud computing infrastructures and networks with adequate capabilities to leverage the next generation of Internet services and applications. The scope of end-to-end services at the Internet scale is exceptionally broad and requires contributions across multiple disciplines along with large investments in capital and human resources.

The current structure of society is extremely dependent on Internet-based services. This reality has given rise to the need for the deployment of smart environments and 5G networks. In this new reality, Internet Service Providers (ISPs) need to deal with various types of scenarios, each with its unique behavior, where two aspects are crucial: Security and Resource Scaling. However, existing solutions do not address the key aspects for the feasible deployment and performance of 5G networks and smart environments in terms of security and resource scaling. Within this context, this project aims to enhance the resource management and security capabilities of ISPs to address the requirements of 5G, such as high connectivity, scalability, technology heterogeneity, and smart environments, through the development of distributed mechanisms and protocols for Network Traffic Profile Identification. This is intended to enable the development of network security and resource scaling solutions for ISPs.

The goal of this project is to train and educate human resources in 5G networks with the aim of contributing to its development in the Information Technology sector and, consequently, expanding innovation in this area. These desired outcomes are based on the current need that the country has for investments in this technology to meet its current and future demands

Currently, Brazil has nearly seven thousand hospitals. However, only about a hundred of them use the Electronic Patient Record (EPR). The EPR is a standardized model for accessing, recording, storing, and managing patient information in healthcare units. Despite the existing need, the majority of healthcare units in Brazil do not have a Health Management System. Currently, new technologies have emerged to support the implementation of Health Management Systems, such as Cloud Computing and the widespread availability of Internet access. Within this context, this project aims to develop a healthcare system to meet the requirements of the Electronic Patient Record (EPR) based on Huawei Cloud.

Many companies and government institutions tend to implement online and cloud services in order to modernize their respective business models, but they are subject to intrusion attempts and data leaks. This project presents an innovative solution regarding data protection and privacy. It will enable companies and government institutions to store and/or share data without violating the points outlined by privacy laws, preventing them from facing the sanctions specified by the law, even in the event of data breaches.

The Ipê Network Monitoring Service (MonIpê) is yet another tool from the National Research and Education Network (RNP) that provides regular performance test results for the network backbone. However, during its execution, MonIpê experiences measurement failures, which generate gaps in the monitoring process and consequently hinder the development of more complex solutions, such as network performance prediction tools. Within this context, this project aims to develop a solution that collects monitoring data from MonIpê, identifies measurement issues, and corrects them to be used as input in the prediction model created by the proponents, enabling performance prediction among the various Points of Presence (PoP) of RNP.

The Ipê Network Monitoring Service (MonIpê) is yet another tool offered by the National Research and Education Network (RNP) that provides regular performance test results for the network backbone. However, it is considered that the tool still needs to evolve in various aspects, such as identifying and addressing network performance issues based on monitoring data. Within this context, this project aims to develop a tool that collects monitoring data from MonIpê and analyzes this data to identify performance issues and correlations among this data, enabling network administrators to adjust communication routes in the most appropriate manner.

Society is dependent on Internet-based services, leading to the emergence of the need for the deployment of intelligent environments based on 5G networks. These environments consist of a large number of heterogeneous wireless devices to support the resources and services offered to users. In this new reality, Internet Service Providers (ISPs) via 5G networks need to deal with various types of scenarios, each with its unique behavior, where network management aspects are crucial. However, existing solutions do not address the key aspects for the feasible deployment and performance of intelligent environments based on 5G networks. Within this context, Artificial Intelligence (AI) techniques emerge as an effective approach to bring adaptability, robustness, and precision to 5G Network Management. In this scenario, this project aims to study and propose new AI-based network management solutions for intelligent environments in 5G networks, such as high connectivity, scalability, and technology heterogeneity

Over the years, the Internet has become the main means of communication, where users expect to have access to the Internet everywhere, at all times, and with quality, through wireless access networks. As a consequence, in recent years the demand for resources for Internet access in edge and access networks around the world has increased, generating a scenario of elastic demand for network resources. Based on this, the scientific community projects that the application of network virtualization (NV), software-defined networking (SDN) and network function virtualization (NFV) technologies are possible solutions to evolve resource management, as well as to make network behavior more flexible and customizable. However, there is still no proposal to manage these technologies in an integrated manner, as well as the various access networks with the edge network of an Internet provider. Within this context, this research project aims to develop a set of mechanisms for the management of Internet providers, expanding the performance of the aforementioned technologies to edge and access networks, integrating them in a cohesive and effective manner. From this, it is expected to improve the user experience regarding Internet access, as well as the use of resources and service provision by Internet providers.

Nowadays, people are surrounded by communication devices that exchange information with each other and with the Internet. This scenario has become increasingly common in homes. As a result, in recent years, the demand for network resources in homes, which are made up of smart devices, has increased. These homes are called Smart Homes. However, there is still no way to manage communication between devices in a Smart Home, since it is necessary to consider several aspects of the wireless environment, such as: type of technology, transmission rate, channel use, frequency, etc. Within this context, this project proposes an architecture, based on software-defined networks, to expand the management capacity of homes in relation to devices in smart homes. The main idea is to abstract access to different types of devices and dynamically adjust the configuration of the devices present in the home, improving the efficiency of device communication and (consequently) the quality of experience of users in their homes.

The Ipê Network Monitoring Service (Monipê) is another tool offered by the National Research and Education Network (RNP) that provides results from regular network backbone performance tests. However, it is considered that the tool still needs to evolve in several aspects, mainly related to more complex activities. Among these is the ability to predict elastic demand for network resources. Within this context, this project aims to develop a tool that collects MonIPÊ monitoring data and structures it to be applied as input in the prediction model created by the proponents, enabling the prediction of demand for resources between the various RNP Points of Presence (PoP).

Recently, several companies have been improving their measurement processes, including the National Research Network (RNP). The implementation of a platform for collecting and making available measurement data is crucial for the evolution of research and products developed. Within this context, this project presents the MicroMon platform based on Microservices for collecting and sharing network measurements.

Currently, a crucial aspect of network management is to identify traffic profiles using Artificial Intelligence (AI) techniques on monitored network data. However, access to information about network traffic can affect users' privacy, thus violating privacy laws. Within this context, the objective of this project is to perform anonymization techniques on network datasets, analyzing their impact on the traffic identification capacity of AI techniques. As a result, we expect to have a report identifying the feasibility of using anonymization in real environments.

Today's smart environments are composed of Internet of Things (IoT) devices and end-user devices (laptops, tablets, etc.) communicating with each other and with the Internet. IoT devices are subject to anomalous behavior (non-standard operation) due to security vulnerabilities or malfunctions. Monitoring the behavior of these devices becomes crucial to ensure efficient network performance. In this context, this project aims to develop a system for traffic monitoring and anomaly detection in smart environments supported by Artificial Intelligence (AI), generating a network profile and detecting possible anomalies through traffic behavior outside the expected pattern.

This project aims to evaluate the effectiveness of a system, called E-HEALTH_SYS, for classifying diseases in the electrocardiogram (ECG) exam and early detection of vital signs (VS) indicative of sepsis. A prospective, observational cohort study that will be carried out in the inpatient units of tertiary hospitals in Fortaleza-CE. The population will be hospitalized patients with cardiac and VS monitoring. Data collection will occur through E-HEALTH_SYS, which, with the information obtained from sensors and VS collected from patients, will be able to interpret the ECG and VS of patients in real time, using cloud computing and artificial intelligence, signaling to health professionals about the patient's health and early detection of sepsis.

In recent years, urban transportation has become a daily topic for Brazilians living in large and medium-sized cities. Traffic jams, traffic accidents, poor quality public transportation, parking difficulties, the new Brazilian Traffic Code, its innovations, violations and penalties have become the focus of the media, the subject of political and educational campaigns, the target of popular movements and the subject of informal conversations. The application of information technology, combined with the planning, management, operation and monitoring of urban transportation, has emerged as a viable alternative in terms of cost-effectiveness, in addition to contributing to meeting the essential sustainability characteristics of the transportation sector, including reducing time lost in traffic jams, traffic accidents, transportation costs, energy consumption and the population's quality of life. This new approach, called Intelligent Transportation Systems (ITS), is expanding in developed countries and is beginning to take its first steps in Brazil. Within this context, this project aims to study the application of ITSs to improve urban mobility and the population's quality of life, evaluating service quality alternatives, as well as the application of an ITS in the city of Fortaleza.