In the fast-progressing field of wireless communications, the forthcoming 6G networks are expected to revolutionize the way we communicate, offering unparalleled speed, minimal latency, and seamless connectivity. However, amid this evolution, the paramount concern remains the security and privacy of the data traversing these networks. Traditional centralized artificial intelligence (AI) techniques already struggle to keep up with the vast amount of data of future 6G networks and deal with the increasing worries about privacy. Federated learning (FL), emerges as a key enabler of Trustworthy AI (TAI), empowering the engagement of distributed network nodes in AI training without the need for exchanging raw data, thereby mitigating the risks associated with centralized data processing. In this paper, we provide a comprehensive survey on the potential of FL in enhancing the security of 6G networks. Particularly, we begin by providing the necessary background on 5G networks and FL, setting the stage for understanding their current and future implications. We then explore the current state-of-the-art of FL applications within 5G networks and their relevance to the future threat landscape of 6G. Subsequently, we examine the inherent vulnerabilities of FL systems, major attacks against FL in the context of 5G networks, and corresponding defense mechanisms. Finally, we discuss the integration of advanced FL technologies and concepts towards enhanced cybersecurity and privacy in 6G networks, aiming to cover all aspects and future perspectives of FL within the context of the forthcoming 6G threat landscape.

Recent studies report that cybersecurity breaches noticed in hospitals are associated with low levels of personnel’s cybersecurity awareness. This work aims to assess the cybersecurity culture in healthcare institutions from middle- to low-income EU countries. The evaluation process was designed and performed via anonymous online surveys targeting individually ICT (internet and communication technology) departments and healthcare professionals. The study was conducted in 2019 for a health region in Greece, with a significant number of hospitals and health centers, a large hospital in Portugal, and a medical clinic in Romania, with 53.6% and 6.71% response rates for the ICT and healthcare professionals, respectively. Its findings indicate the necessity of establishing individual cybersecurity departments to monitor assets and attitudes while underlying the importance of continuous security awareness training programs. The analysis of our results assists in comprehending the countermeasures, which have been implemented in the healthcare institutions, and consequently enhancing cybersecurity defense, while reducing the risk surface.

The coronavirus pandemic led to an unprecedented crisis affecting all aspects of the concurrent reality. Its consequences vary from political and societal to technical and economic. These side effects provided fertile ground for a noticeable cyber-crime increase targeting critical infrastructures and, more specifically, the health sector; the domain suffering the most during the pandemic. This paper aims to assess the cybersecurity culture readiness of hospitals’ workforce during the COVID-19 crisis. Towards that end, a cybersecurity awareness webinar was held in December 2020 targeting Greek Healthcare Institutions. Concepts of cybersecurity policies, standards, best practices, and solutions were addressed. Its effectiveness was evaluated via a two-step procedure. Firstly, an anonymous questionnaire was distributed at the end of the webinar and voluntarily answered by attendees to assess the comprehension level of the presented cybersecurity aspects. Secondly, a post-evaluation phishing campaign was conducted approximately four months after the webinar, addressing non-medical employees. The main goal was to identify security awareness weaknesses and assist in drafting targeted assessment campaigns specifically tailored to the health domain needs. This paper analyses in detail the results of the aforementioned approaches while also outlining the lessons learned along with the future scientific routes deriving from this research.

Fifth generation (5G) networks have significantly enhanced connectivity, speed, and reliability, transforming industries with faster and more efficient communication. The Internet of Things (IoT) has introduced unprecedented convenience and automation, revolutionizing sectors such as healthcare, finance, and smart infrastructure. However, both 5G networks and IoT environments are experiencing a high frequency of attacks. Intrusion detection systems (IDSs) built on federated learning (FL) are being proposed to boost data privacy and security. However, these IDSs are related with the inherent drawbacks of FL, namely the existence of non-independently and identically (non-IID) distributed features and the machine learning model complexity. To address these limitations, we present a study that integrates a Mixture of Experts (MoE) into an FL setting in the task of intrusion detection. Specifically, to mitigate the issues of model complexity within the FL setting, we use a sparsely gated MoE layer consisting of a router/gating network and a set of experts. Only a subset of experts is selected via applying noisy top-k gating. To alleviate the issue of non-IID data, we adopt the Label-based Dirichlet Partition method, utilizing Dirichlet sampling with a hyperparameter $\alpha$ to simulate a label-based non-IID data distribution. Four FL strategies are employed. We perform our experiments on the 5G-NIDD and BoT-IoT datasets. Findings show that the proposed approach achieves competitive performance across both datasets under heterogeneous federated settings.

The evolution of Beyond 5G (B5G) and 6G networks requires the design of powerful deep neural networks to effectively address the emerging threats and ensure predictive, adaptive security measures that can safeguard the performance and integrity of these advanced systems. Existing studies rely on fully supervised learning settings, lack mechanisms for dynamic adaptation, and perform their experiments on datasets, which do not represent the characteristics of existing 5G scenarios. To address these limitations, we present the first study incorporating hypernetworks into convolutional autoencoders for identifying attacks in 5G datasets collected under realistic conditions. Specifically, the input data is reshaped into a 2D matrix and fed into a convolutional autoencoder, consisting of an encoder and a decoder. At the same time, raw input data is passed through a hypernetwork, which is trained to generate weights for the target network. The target network receives as input the latent representation vector and gives as output the final prediction. Experiments are performed on two real 5G network datasets, namely NANCY and 5G-NIDD. Results show that the proposed deep neural network achieves an Accuracy of 98.51% and 99.91% on the NANCY and 5G-NIDD datasets respectively. Findings of an ablation study demonstrate the effectiveness of the proposed method.