Abstract 6G networks are envisioned to provide an extremely high quality‐of‐service (QoS). Then, future 6G network must operate in the Ka‐band, where more bandwidth and radio channels are available, and noise and interferences are lower. But even in this context, 6G base stations must adjust the transmission power to ensure the signal‐to‐noise ratio is good enough to enable the expected QoS. However, 6G networks are not the only infrastructure operating in that band. Actually, many scientific instruments are also working on those frequencies. Considering that 6G networks will be transmitting a relevant power level, they can interfere very easily with these scientific instruments. Therefore, in this paper we propose a new solution to enable the interferenceless coexistence between 6G networks and scientific instruments. This solution includes a three‐dimensional model to analyse future positions of user devices. Using this information and an interference model, we design a decision model to adapt the transmitted power, so the QoS achieves the expected level. Besides, when the transmitted power is high enough to interfere with close scientific instruments, a scheduling algorithm based on swarm intelligence is triggered. This algorithm calculates the optimum distribution of time slots and radio channels, so the scientific instruments can operate, and the 6G networks can still provide the required QoS. An experimental validation is provided to analyse the performance of the proposed solution. Results show a complete coexistence may be achieved with an interference level of −26 dBm and a QoS above 95% of the expected level.

Abstract Ambient Intelligence deployments are very vulnerable to Cyber-Physical attacks. In these attacking strategies, intruders try to manipulate the behavior of the global system by affecting some key elements within the deployment. Typically, attackers inject false information, integrate malicious devices within the deployment, or infect communications among sensor nodes, among other possibilities. To protect Ambient Intelligence deployments against these attacks, complex data analysis algorithms are usually employed in the cloud to remove anomalous information from historical series. However, this approach presents two main problems. First, it requires all Ambient Intelligence systems to be networked and connected to the cloud. But most new applications for Ambient Intelligence are supported by isolated systems. And second, they are computationally heavy and not compatible with new decentralized architectures. Therefore, in this paper we propose a new decentralized security solution, based on a Blockchain ledger, to protect isolated Ambient Intelligence deployments. In this ledger, new sensing data are considered transactions that must be validated by edge managers, which operate a Blockchain network. This validation is based on reputation metrics evaluated by sensor nodes using historical network data and identity parameters. Through information theory, the coherence of all transactions with the behavior of the historical deployment is also analyzed and considered in the validation algorithm. The relevance of edge managers in the Blockchain network is also weighted considering the knowledge they have about the deployment. An experimental validation, supported by simulation tools and scenarios, is also described. Results show that up to 93% of Cyber-Physical attacks are correctly detected and stopped, with a maximum delay of 37 s.

Data analytics has emerged as a critical capability for businesses and organizations in the modern era. The abundance of data necessitates a deep understanding and the exploitation of its potential to gain insights into current and future scenarios.

Cybersecurity is one of the most promising areas for future graduated students to develop their work life. However, the weight of cybersecurity is still low in most computer engineering programs, even though computer engineers are the most suitable professionals for cybersecurity activities. We claim the implementation of cybersecurity competitions, where students participate in teams between four and six people, improving the acquisition of cybersecurity competencies through the promotion of the autonomous, collaborative, and personalized learning. In order to validate the hypothesis, an experience was conducted during the second term of 2020/2021 course in Universidad Politécnica de Madrid. Experience was conducted in the computer engineering degree, in the context of the subject “Cybersecurity in Systems and Networks”. Two groups were defined: the control group following a traditional learning methodology, and a pilot group where learning was based on a cybersecurity competition, including short questionnaires, laboratory practices and puzzles. The results obtained validate the hypothesis and answer all research questions in a positive way. All learning indicators improve when students work on cybersecurity competencies through an educational competition.