Over the past five years, interest in the literature regarding the security of the Internet of Medical Things (IoMT) has increased. Due to the enhanced interconnectedness of IoMT devices, their susceptibility to cyber-attacks has proportionally escalated. Motivated by the promising potential of AI-related technologies to improve certain cybersecurity measures, we present a comprehensive review of this emerging field. In this review, we attempt to bridge the corresponding literature gap regarding modern cybersecurity technologies that deploy AI techniques to improve their performance and compensate for security and privacy vulnerabilities. In this direction, we have systematically gathered and classified the extensive research on this topic. Our findings highlight the fact that the integration of machine learning (ML) and deep learning (DL) techniques improves both the performance of cybersecurity measures and their speed, reliability, and effectiveness. This may be proven to be useful for improving the security and privacy of IoMT devices. Furthermore, by considering the numerous advantages of AI technologies as opposed to their core cybersecurity counterparts, including blockchain, anomaly detection, homomorphic encryption, differential privacy, federated learning, and so on, we provide a structured overview of the current scientific trends. We conclude with considerations for future research, emphasizing the promising potential of AI-driven cybersecurity in the IoMT landscape, especially in patient data protection and in data-driven healthcare.

Federated learning (FL) has emerged as a pivotal paradigm for training machine learning models across decentralized devices while maintaining data privacy. In the healthcare domain, FL enables collaborative training among diverse medical devices and institutions, enhancing model robustness and generalizability without compromising patient privacy. In this paper, we propose DPS-GAT, a novel approach integrating graph attention networks (GATs) with differentially private client selection and resource allocation strategies in FL. Our methodology addresses the challenges of data heterogeneity and limited communication resources inherent in medical applications. By employing graph neural networks (GNNs), we effectively capture the relational structures among clients, optimizing the selection process and ensuring efficient resource distribution. Differential privacy mechanisms are incorporated, to safeguard sensitive information throughout the training process. Our extensive experiments, based on the Regensburg pediatric appendicitis open dataset, demonstrated the superiority of our approach, in terms of model accuracy, privacy preservation, and resource efficiency, compared to traditional FL methods. The ability of DPS-GAT to maintain a high and stable number of client selections across various rounds and differential privacy budgets has significant practical implications, indicating that FL systems can achieve strong privacy guarantees without compromising client engagement and model performance. This balance is essential for real-world applications where both privacy and performance are paramount. This study suggests a promising direction for more secure and efficient FL medical applications, which could improve patient care through enhanced predictive models and collaborative data utilization.

MDCG 2019-16 is intended to assist practitioners in compliance with the Medical Device Regulation and the In-Vitro Device Regulation. This paper presents a gap analysis of MDCG 2019-16, identifying key gaps and proposing a robust set of recommendations to enhance the IoMT regulatory framework. This work has been undertaken by a selection of current (2023-2025) projects, all funded under the Horizon Europe call “Enhancing cybersecurity of connected medical devices”: HORIZON-HLTH-2022-IND-13-01, and this paper summarises observations and recommendations from across these projects. There is considerable consensus across the projects in many recommendation themes, notably; linking cybersecurity with patient safety and privacy; keeping the guidelines current; and usage recipes for the guidelines. The paper also suggests toolkit solutions to address some of the recommendations.

In this work, we introduce QuanGAT, a hybrid framework integrating quantum neural networks (QNNs), graph attention networks (GATs), and federated learning to tackle DNA mutation prediction in biomedical graphs in a privacy-preserving, noise-aware setting. Motivated by quantum biology’s potential to describe mutation-level mechanisms, our approach is able to simulate decentralized genomic environments in order to protect sensitive data, while capturing complex biological variability. QuanGAT employs a QNN encoder based on parameterized quantum circuits, incorporating quantum noise, via the so-called depolarizing and amplitude damping channels. Furthermore, in order to enforce differential privacy, QuanGAT utilizes an attention-based architecture enhanced with Laplace noise. We evaluated the model on mutation-enriched protein–protein interaction networks, partitioned across simulated clients under centralized, federated, and noisy scenarios. In particular, we employed QuanGAT in three different datasets, namely PPI, STRING, and OBGN-Proteins. Especially in federated learning settings, QuanGAT consistently outperformed certain state-of-the-art graph neural networks up to 4.5 % in terms of accuracy and up to 6.3 % in terms of macro F1 score. Our findings demonstrate that integrating QNN encodings with attention-based graph learning may improve DNA mutation prediction in decentralized, privacy-aware settings.

Future-facing cities increasingly integrate smart and autonomous objects for their smooth functioning and operations, which ultimately benefit city dwellers and the ecosystem at large. In such highly complex and digital environments, the increased situational awareness is very important for the safety of road participants. In this paper, we propose a new bandwidth-aware framework that maximizes the situational awareness of a given region, using mobile digital boxes and $360^{\circ }$ cameras, mounted on connected vehicles, taking into account the constrained uplink capacity. The proposed framework leverages the multi-view spectral clustering approach and the K-Means++ algorithms to ensure efficient clustering of vehicles based on their GPS coordinates. The clustering step is crucial for larger spatial coverage and, thus, higher situational awareness. Vehicle selection and video quality attribution, under limited uplink constraints, are then performed per cluster to fairly cover the region. Extensive simulations and comparisons against state-of-the-art solutions have been conducted to evaluate the performance of the proposed framework, in terms of region coverage rate and normalized mutual information score, at both small- and large-scale deployments. The results obtained demonstrate the superiority of the proposed approach.

This work proposes Zeekflow+, a Deep LSTM Autoencoder (AE) architecture with integrated Random Forest (RF) classifier for effective binary & multi-class classification of network traffic data. The Deep LSTM AE is used to extract underlying patterns existing within the features of the input data and encode them accordingly, allowing for binary classification to malicious or benign behavior. Through the use of the RF classifier, the binary classification capabilities are extended to multi-class ones for effectively identifying the origin of the attacks. This is achieved by training the RF classifier using the total reconstruction loss and the Deep LSTM AE encoded data. Experimental results on the USTC-TFC2016 dataset, showcase the performance of the Zeekflow+ architecture in multi-class classification resulting in more than 99% in the precision, recall and F1-Score classification metrics. To further demonstrate the effectiveness of the Zeekflow+ architecture, it is used for binary classification task in the same dataset, while considering different Floating Point arithmetic quantizations with the results showing negligible performance drop, making it suitable for real-time IoT edge device deployment.