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
In mobile networks, 5G Ultra-Dense Networks (UDNs) have emerged as they effectively increase the network capacity due to cell splitting and densification. A Base Station (BS) is a fixed transceiver that is the main communication point for one or more wireless mobile client devices. As UDNs are densely deployed, the number of BSs and communication links is dense, raising concerns about resource management with regard to energy efficiency, since BSs consume much of the total cost of energy in a cellular network. It is expected that 6G next-generation mobile networks will include technologies such as artificial intelligence as a service and focus on energy efficiency. Using machine learning it is possible to optimize energy consumption with cognitive management of dormant, inactive and active states of network elements. Reinforcement learning enables policies that allow sleep mode techniques to gradually deactivate or activate components of BSs and decrease BS energy consumption. In this work, a sleep mode management based on State Action Reward State Action (SARSA) is proposed, which allows the use of specific metrics to find the best tradeoff between energy reduction and Quality of Service (QoS) constraints. The results of the simulations show that, depending on the target of the 5G use case, in low traffic load scenarios and when a reduction in energy consumption is preferred over QoS, it is possible to achieve energy savings up to 80% with 50 ms latency, 75% with 20 ms and 10 ms latencies and 20% with 1 ms latency. If the QoS is preferred, then the energy savings reach a maximum of 5% with minimal impact in terms of latency.

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
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
Task scheduling is a significant challenge in the cloud environment as it affects the network's performance regarding the workload of the cloud machines. It also directly impacts the consumed energy, therefore the profit of the cloud provider. This paper proposed an algorithm that prioritizes the tasks regarding their execution deadline. We also categorize the physical machines considering their configuration status. Henceforth, the proposed method assigns the jobs to the physical machines with the same priority class close to the user. Furthermore, we reduce the consumed energy of the machines processing the low-priority tasks using the DVFS method. The proposed method migrates the jobs to maintain the workload balance, or if the machines' class changed according to their scores. We have evaluated and validated the proposed method in the CloudSim library. The simulation results demonstrate that the proposed method optimized energy consumption by 12% and power consumption by 20%.

Abstract
Currently, there are several security-related standards and recommendations concerning Domain Name System (DNS) and Hypertext Transfer Protocol (HTTP) services, that are highly valuable for governments and their services, and other public or private organizations. This is also the case of Higher Education Institutions (HEIs). However, since these institutions have administrative autonomy, they present different statuses and paces in the adoption of these web-related security services. This paper presents an overview regarding the implementation of security standards and recommendations by the Portuguese HEIs. In order to collect these results, a set of scripts were developed and executed. Data were collected concerning the security of the DNS and HTTP protocols, namely, the support of Domain Name System Security Extensions (DNSSEC), HTTP main configurations and redirection, digital certificates, key size, algorithms and Secure Socket Layer (SSL)/Transport Layer Security (TLS) versions used. The results obtained allow to conclude that there are different progresses between HEIs. In particular, only 11.7% of HEIs support DNSSEC, 14.4% do not use any SSL certificates, 74.8% use a 2048 bits encryption key, and 81.1% use the Rivest-Shamir-Adleman (RSA) algorithm. Also, 6.3% of HEIs still negotiate with the vulnerable SSLv3 version. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.