Distributed Denial of Service (DDoS) attacks are a global security concern, driven by the rapid digital transformation and the widespread adoption of 5G networks across industries. As networks connect more devices to the Internet, enabling use cases such as the industrial Internet of Things (IoT), the attack surface grows. Thus, applying deep learning techniques presents a promising approach for detecting DDoS attacks. For example, using the CIC-DDoS2019 dataset, a Convolutional Neural Network (CNN) model with multi-head attention achieved 97% accuracy. With a 5G slice-based dataset, the model achieved an accuracy of 91%. These results highlight the potential of CNNs as a scalable approach to mitigating DDoS threats. Beyond improving detection accuracy, this method enables real-time identification of attacks, supporting a proactive defense against evolving cybersecurity threats. The findings demonstrate that CNNs can help secure critical infrastructure, including IoT devices, telecommunications systems, and other networked services. Implementing such solutions enhances network security and strengthens resilience against emerging challenges.

Over the years, researchers have explored the influence of human factors in software engineering, showing that the team members' personalities might affect teamwork.However, it is challenging to measure software engineers' personalities due to the number of available psychometric instruments and the possibility of using different scales and classifications.Our study compares the personality traits measured by three psychometric instruments used in Software Engineering: Big Five Inventory (BFI), 16 Personality Factors (16PF), and Context Cards (CC).For this purpose, we executed an empirical study in which we collected data from 29 software developers for each of the evaluated instruments.As a result, we identified a moderate correlation between BFI and 16PF, confirming the current stateof-the-art.For the remaining combinations, there was a weak correlation.As implications for this research, there is a need to empirically evaluate BFI and CC (context-specific survey) in terms of construct validity since they have moderate to low correlation.

The development of tools and smart devices has grown exponentially over the past few years, most due to the appearance of the Internet of Things (IoT).The large number of different connected devices highlighted challenges such as interoperability, scalability and reliability.In this scenario, for the development of new services and applications, it is necessary to use software platforms that ease the deployment and validation based on those challenges.With this, arises the need for middlewares, platforms that provide an environment for the development of such applications.In this way, in order to provide a simple, lightweight and dynamic approach, this article presents a study and development of an architecture that allows the communication, control and monitoring of IoT devices using an intuitive manner through an actor model.This is possible through the integration of the Calvin framework, the MQTT protocol and the OCF data model, providing an interoperable, reliable, dynamic and remote communication.A smart home environment was used for validation showing the relevance of the proposal.

In software-intensive products, evidences of regulation conformance and failure propagation mitigation are required. The use of Traceability Information Model (TIM) is an important practice to support system engineering artifacts tracing and links, becoming part of a project traceability planning. Despite the relevance of traceability in terms of guaranteeing quality attributes and certification of these products, improvements creating a link between safety and systems engineering artifacts have not been focus of attention in the connected health industry. The lack of reference practices for adequately applying traceability has hindered product certification. In this work, we move towards introducing a TIM practice for integrated functional and safety specifications in connected health projects. To achieve this, we performed a case study involving an infusion control system. Evidences show that considerable advantages can be obtained by introducing a TIM into the modeling process. Therefore, the main contribution of this paper is to present an approach for the connected health industry to start dealing with traceability challenges.

Health information systems are increasing in quantity and complexity. In this sense, projects of health information systems, which are well-fitted for agile, start to face new challenges imposed by regulators, society and suppliers since they start to interact with medical devices. The problem is that the development of medical device software presents requirements that normally are not addressed by agile methodologies. In this paper, we present RE4CH (Requirement Engineering for Connected Health), an agile process focused on the health regulation viewpoint. We introduce its activities and show its application in an open source case study. Choosing to focus on traceability, we take an important step in compliance following agile practices for open source software that is highly regulated and safety-critical.

One of the main issues of an agile software project is how to accurately estimate development effort.In 2014, it was published a Systematic Literature Review (SLR) regarding this subject.The authors of this SLR analyzed works from 2001 to 2013 and reached the number of 25 relevant papers.Therefore, the goal of our work is to provide an updated review of the state of the art based on this reference SLR work.We applied a Forward Snowballing approach, in which our seed set are the former SLR and its selected papers.We identified changes in this new review comparing it with the reference SLR: XP methodology was mentioned in just a few works; Use Case Points (UCP) method and Case Points as size metric were not found.We also observed a strong indication of solutions based on Artificial Intelligence and Machine Learning methods for effort estimation in Agile Software Development (ASD).Finally, we identified that in the reference SLR there is a gap in terms of agreement on suitable cost drivers.Thus, in our updated review, we applied Thematic Analysis in the selected papers and identified a representative set of 10 cost drivers for effort estimation.

One of the main issues of an agile software project is how to accurately estimate development effort. In 2014, a Systematic Literature Review (SLR) regarding this subject was published. The authors concluded that there were several gaps in the literature, such as the low level of accuracy of the techniques and little consensus on appropriate cost drivers. The goal of our work is to provide an updated review of the state of the art based on this reference SLR work. We applied a Forward Snowballing approach, in which our seed set included the former SLR and its selected papers. We identified a strong indication of solutions based on Artificial Intelligence and Machine Learning methods for effort estimation in Agile Software Development (ASD). We also identified that there is a gap in terms of agreement on suitable cost drivers. Thus, we applied Thematic Analysis in the selected papers and identified a representative set of 10 cost drivers for effort estimation. This updated review of the state of the art resulted in 24 new relevant papers selected.

Personal Health Devices (PHDs) with wireless technologies are becoming popular for remotely monitoring patients. However, although these devices are portable and mostly used at home, their integration with Consumer Electronics (CE) devices and networks is still in the earlier steps. This article presents a reference architecture that integrates at home PHDs and CE devices based on the UPnP (Universal Plug and Play) technology and the IEEE 11073 set of standards. This article introduces a UPnP device architecture for personal m-Health (mobile Health), describing how different types of devices interact to exchange personal health information. One of the main features of this work is the use of widely adopted data formats, such as eXtended Markup Language (XML) for health information exchange based on IEEE 11073 data model. Such approach enables web based CE networks, such as UPnP, to interact with PHD devices efficiently.

The increasing availability of connected Personal Health Devices (PHDs) enables a new type of information to be available in the Internet: health information. Most of these devices have specific ways to connect and share information to the Internet through gateways or health managers, creating vertical solutions where one device just talks to one health service. In this context, this paper proposes an architecture that considers the use of different types of health managers and gateways, but keeping interoperability by the use of widely adopted standards. The main contribution of this work is the distribution of health managers in different locations, such as mobile devices and cloud applications, enabling the use of a single health service for different types of PHDs. The ISO/IEEE 11073 standard is used as core technology, enabling the transport of PHD information over different technologies and protocols. We also present a new classification of health managers based on requirements of legacy m-health services. In conclusion, the results of the integration with a real cloud-based connected health system are presented and evaluated.

This paper presents the BRisa UPnP A/V framework. BRisa is a framework that allows users to discover multimedia devices, share, search and render multimedia content over the local networks or remotely through the Internet. It has been developed using UPnP specifications, which makes use of standard Internet protocols and services, such as HTTP, UDP and SOAP. BRisa is composed of three main applications: the BRisa Media Server, the BRisa Media Renderer and the BRisa Control Point, each of them plays a specific role in our entire UPnP solution. In this paper we briefly describe our UPnP implementation and discuss a set of features implemented to share audio/video/image in a computer network. We also explain the application of our approach to resource limited devices, more specifically for the N800 Nokia Internet Tablet.