Abstract
An intrusion detection system (IDS) is an important tool to prevent potential threats to systems and data. Anomaly-based IDSs may deploy machine learning algorithms to classify events either as normal or anomalous and trigger the adequate response. When using supervised learning, these algorithms require classified, rich, and recent datasets. Thus, to foster the performance of these machine learning models, datasets can be generated from different sources in a collaborative approach, and trained with multiple algorithms. This paper proposes a vote-based architecture to generate classified datasets and improve the performance of supervised learning-based IDSs. On a regular basis, multiple IDSs in different locations send their logs to a central system that combines and classifies them using different machine learning models and a majority vote system. Then, it generates a new and classified dataset, which is trained to obtain the best updated model to be integrated into the IDS of the companies involved. The proposed architecture trains multiple times with several algorithms. To shorten the overall runtimes, the proposed architecture was deployed in Fed4FIRE+ with Ray to distribute the tasks by the available resources. A set of machine learning algorithms and the proposed architecture were assessed. When compared with a baseline scenario, the proposed architecture enabled to increase the accuracy by 11.5% and the precision by 11.2%.

Abstract
The government and industry have given the recent development of the Internet of Things in the healthcare sector significant respect. Health service providers retain data gathered from many sources and are useful for patient diagnostics and research for pivotal analysis. However, sensitive personal information about a person is contained in healthcare data, which must be protected. Individual privacy protection is a crucial concern for both people and organizations, particularly when those firms must send user data to data centers due to data mining. This article investigated two general states of increasing entropy by changing the entropy of the class set of characteristics based on artificial intelligence and the k-anonymity model in privacy in context, and also three different strategies have been investigated, i.e., the strategy of selecting the feature with the lowest number of distinct values, selecting the feature with the lowest entropy, and selecting the feature with the highest entropy. For future tasks, we can find an optimal strategy that can help us to achieve optimal entropy in the least possible repetition. The results of our work have been compared by lightweight and MH-Internet of Things, FRUIT methods and shown that the proposed method has high efficiency in entropy criteria.

Abstract
The services supporting the websites, both public and private entities, may support security protocols such as HTTPS or DNSSEC. Public and private entities have a responsibility to ensure the security of their online platforms. Entities in the public domain such as city councils provide their services through their websites. However, each city council has its systems, configurations, and IT teams, and this means they have different standings regarding the security protocols supported. This paper analyzes the status of security protocols on Portuguese city council websites, specifically HTTPS and DNSSEC. The study evaluated 308 city council websites using a script developed for the research, and data was collected from the website of Direcao Geral das Autarquias Locais (DGAL) on December 14, 2022, and the websites were scanned on December 22, 2022. The results of this assessment reveal that around 97% of city council websites use RSA as their encryption algorithm and around 84% use 2048-bit length keys for digital certificate signing. Furthermore, about 53% of the city council websites are still supporting outdated and potentially insecure SSL/TLS versions, and around 95% of the councils are not implementing DNSSEC in their domains. These results highlight potential areas for improvement in cybersecurity measures and can serve as a baseline to track progress toward improving cybersecurity maturity in Portuguese city councils.

Abstract
The General Data Protection Regulation (GDPR) is the regulation that determines the directives inherent to the collection, processing, and protection of personal data in European Union (EU) countries. It was implemented in May 2018 and over the past few years, several public and private companies have been affected by serious penalties. With more than 1500 fines already registered, it is important to have an analysis and insights about them. This paper proposes a detailed analysis of the public records of fines under GDPR, understanding the average fines imposed, the main causes for their application and how they have evolved over time. It is also intended to understand the most affected sectors and point ways to mitigate these penalties. It is concluded that fines under GDPR have an increasing trend over time, both in number of fines and in value, with Industry and Commerce & Media, Telecoms and Broadcasting being the most affected sectors.

Abstract
With the introduction of Industry 4.0 technological concepts, suppliers and manufacturers envision new or improved products and services, cost reductions, and productivity gains. In this context, data exchanges between companies in the same or different activity sectors are necessary, while assuring data security and sovereignty. Thus, it is crucial to select and implement adequate standards which enable the interconnection requirements between companies and also feature security by design. The International Data Spaces (IDS) is a current standard that provides data sharing through data spaces mainly composed of homogeneous rules, certified data providers/consumers, and reliability between partners. Implementing IDS in sectors such as textile and clothing is expected to open new opportunities and challenges. This paper proposes a prototype for the IDS Security Components in the Textile and Clothing Industry context. This prototype assures data sovereignty and enables the interactions required by all participants in this supply chain industry using secure communications. The adoption of IDS as a base model in this activity sector fosters productive collaboration, lowers entry barriers for business partnerships, and enables an innovation environment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
In the past years, the automotive industry has been integrating multiple hardware in the vehicle to enable new features and applications. In particular automotive applications, it is important to monitor the actions and behaviors of drivers and passengers to promote their safety and track abnormal situations such as social disorders or crimes. These applications rely on multiple sensors that generate real-time data to be processed, and thus, they require adequate data acquisition and analysis systems.This article proposes a prototype to enable in-vehicle data acquisition and analysis based on the middleware framework Robot Operating System (ROS). The proposed prototype features two processing devices and enables synchronized audio and video acquisition, storage, and processing. It was assessed through the implementation of a live inference system consisting of a face detection algorithm from the data gathered from the cameras and the microphone. The proposed prototype inherits the flexibility of the ROS framework and has a modular and scalable design; thus, more sensors, processing devices, and applications can be deployed.