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.

This paper explores the security challenges associated with network slicing in 5th Generation (5G) networks, a technology that enables the creation of virtual networks tailored to different use cases. This study contributes to network slicing research efforts by providing a comprehensive classification of attacks aligned with the architectural layers of 5G, complemented by practical mitigation approaches suitable for multi-tenant environments. The classification depicts specific attacks and categorizes vulnerabilities across layers such as orchestration, virtualization, and inter-slice communication. Additionally, mitigation strategies are discussed, emphasizing the importance of real-time monitoring and robust access controls. The proposed classification aims to support the development of advanced security mechanisms, including risk assessment models and automated mitigation strategies, tailored to the dynamic and heterogeneous nature of 5G slicing. The findings highlight the need for layered defenses, AI-driven monitoring, and architectural isolation as critical components to enhance the resilience of 5G slicing deployments.

Cybersecurity is critical for mitigating the economic and reputational impacts of cyberattacks. To address these risks, frameworks like the NIST Cybersecurity Framework (NIST CSF) provide standardized guidelines for managing and reducing cybersecurity threats. This paper presents a maturity assessment approach aligned with the NIST CSF, incorporating a dual-survey methodology. The first survey engages cybersecurity experts to calibrate question importance, while the second targets organizations across management, IT staff, and other roles. The approach employs algorithms to deliver consistent evaluations and facilitate cross-organization comparisons. Results from case studies illustrate cybersecurity maturity levels for each NIST CSF function and highlight priority controls for enhancing organizational cybersecurity.

The Domain Name Service (DNS) is a central point in the functioning of the internet. Just as organizations use domain names to enable the access to their computational services, malicious actors make use of domain names to point to the services under their control. Distinguishing between non-malicious and malicious domain names is extremely important, as it allows to grant or block the access to external services, maximizing the security of the organization and users. Nowadays there are many DNS firewall solutions. Most of these are based on known malicious domain lists that are being constantly updated. However, in this way, it is only possible to block known malicious communications, leaving out many others that can be malicious but are not known. Adopting machine learning to classify domains contributes to the detection of domains that are not yet on the block list. The dataset described in this manuscript is meant for supervised machine learning-based analysis of malicious and non-malicious domain names. The dataset was created from scratch, using publicly DNS logs of both malicious and non-malicious domain names. Using the domain name as input, 34 features were obtained. Features like the domain name entropy, number of strange characters and domain name length were obtained directly from the domain name. Other features like, domain name creation date, Internet Protocol (IP), open ports, geolocation were obtained from data enrichment processes (e.g. Open Source Intelligence (OSINT)). The class was determined considering the data source (malicious DNS log files and non-malicious DNS log files). The dataset consists of data from approximately 90000 domain names and it is balanced between 50% non-malicious and 50% of malicious domain names.

Nowadays there are many DNS firewall solutions to prevent users accessing malicious domains. These can provide real-time protection and block illegitimate communications, contributing to the cybersecurity posture of the organizations. Most of these solutions are based on known malicious domain lists that are being constantly updated. However, in this way, it is only possible to block malicious communications for known malicious domains, leaving out many others that are malicious but have not yet been updated in the blocklists. This work provides a study to implement a DNS firewall solution based on ML and so improve the detection of malicious domain requests on the fly. For this purpose, a dataset with 34 features and 90 k records was created based on real DNS logs. The data were enriched using OSINT sources. Exploratory analysis and data preparation steps were carried out, and the final dataset submitted to different Supervised ML algorithms to accurately and quickly classify if a domain request is malicious or not. The results show that the ML algorithms were able to classify the benign and malicious domains with accuracy rates between 89% and 96%, and with a classification time between 0.01 and 3.37 s. The contributions of this study are twofold. In terms of research, a dataset was made public and the methodology can be used by other researchers. In terms of solution, the work provides the baseline to implement an in band DNS firewall.