The growing number of cyber threats and the implementation of stringent privacy regulations have revealed significant shortcomings in traditional centralized machine learning models, especially in distributed systems like the Internet of Things (IoT). This study presents a Federated Learning (FL) framework designed for intrusion detection and malware classification. This framework enables decentralized model training while preserving data locality and minimizing communication overhead. The proposed architecture incorporates lightweight, privacy-preserving techniques, including gradient clipping, differential privacy, and encrypted model aggregation, to ensure secure and efficient collaboration across heterogeneous clients. Experimental results on two widely adopted cybersecurity benchmarks demonstrate that the framework achieves detection accuracies above 90%, maintains privacy loss below 5%, and improves communication efficiency by over 25%. These results confirm the viability of FL as a scalable, privacy-compliant approach for next-generation cybersecurity systems in highly distributed infrastructures.

The increasing frequency of cyber threats and the enforcement of strict privacy regulations have exposed critical limitations of traditional centralized machine learning models, especially in distributed environments such as the Internet of Things (IoT). This study presents a federated learning (FL) framework tailored for intrusion detection and malware classification that enables decentralized model training while preserving data locality and minimizing communication overhead. The proposed architecture incorporates lightweight privacy-preserving techniques-including gradient clipping, differential privacy, and encrypted model aggregation-to ensure secure and efficient collaboration across heterogeneous clients. Experimental results on benchmark datasets, such as CICIDS2017 [1] and TON_IoT [2], show that the framework achieves detection accuracies above 90%, while maintaining privacy loss below 5% and improving communication efficiency by more than 25%. These results confirm the viability of federated learning as a scalable and privacy-compliant approach for next-generation cybersecurity systems in highly distributed infrastructures.

The growing use of mobile health (mHealth) technologies adds complexity and risk to the healthcare environment. This paper presents a multi-layered cybersecurity assessment of an in-house mHealth platform (PHGL-COVID), comprising a Docker-based server infrastructure, a Samsung Galaxy A55 smartphone, and a Galaxy Watch 7 wearable. The objective was to identify vulnerabilities across the server, mobile, and wearable components by emulating real-world attacks and conducting systematic penetration tests on each layer. Tools and methods specifically tailored to each technology were applied, revealing exploitable configurations, insecure Bluetooth Low Energy (BLE) communications, and exposure of Personal Health Records (PHRs). Key findings included incomplete container isolation, BLE metadata leakage, and persistent abuse of Android privacy permissions. This work delivers both a set of actionable recommendations for developers and system architects to strengthen the security of mHealth platforms, and a reproducible audit methodology that has been validated in a real-world deployment, effectively bridging the gap between theoretical threat models and practical cybersecurity practices in healthcare systems.

Cyber-physical infrastructures such as hospitals and smart campuses face hybrid threats that target both digital and physical domains. Traditional security solutions separate surveillance from network monitoring, leaving blind spots when attackers combine these vectors. This paper introduces ARGUS, an autonomous robotic platform designed to close this gap by correlating cyber and physical anomalies in real time. ARGUS integrates computer vision for facial and weapon detection with intrusion detection systems (Snort, Suricata) for monitoring malicious network activity. Operating through an edge-first microservice architecture, it ensures low latency and resilience without reliance on cloud services. Our evaluation covered five scenarios—access control, unauthorized entry, weapon detection, port scanning, and denial-of-service attacks—with each repeated ten times under varied conditions such as low light, occlusion, and crowding. Results show face recognition accuracy of 92.7% (500 samples), weapon detection accuracy of 89.3% (450 samples), and intrusion detection latency below one second, with minimal false positives. Audio analysis of high-risk sounds further enhanced situational awareness. Beyond performance, ARGUS addresses GDPR and ISO 27001 compliance and anticipates adversarial robustness. By unifying cyber and physical detection, ARGUS advances beyond state-of-the-art patrol robots, delivering comprehensive situational awareness and a practical path toward resilient, ethical robotic security.

Smart wearable biomedical sensors/devices provide the ability for continuous monitoring of physiological parameters in occupational settings. Industrial Internet of Things (IIoT)’s integration in wearable biomedical technology presents additional cybersecurity risks and privacy concerns as attack surfaces expand and sensitive biometric data is processed. This scoping review included studies published between 2020 and 2026, screened according to PRISMA-ScR guidelines, which identified 39 articles. The findings indicate that most systems rely on commercial wearables for monitoring fatigue, stress, and environmental conditions, often with limited consideration of the cybersecurity aspects. Common risks include insecure wireless communication, data interception, firmware and supply chain attacks, and vulnerabilities related to IT/OT integration. To address these challenges, this study proposes a multi-layer threat taxonomy covering sensing, communication, processing, AI/analytics, and organizational layers. The results highlight the need for end-to-end cyber-resilient architectures and privacy-by-design approaches in occupational wearable monitoring systems.

The growing use of mobile health (mHealth) technologies adds complexity and risk to the healthcare environment. This paper proposes a multi-layered assessment of cybersecurity of an in-house mHealth platform (PHGL-COVID), which consists of a Docker-based server infrastructure, a Samsung Galaxy A55 smartphone, and a Galaxy Watch 7 wearable. The goal of the work was to perform an assessment of cybersecurity vulnerabilities across the server, mobile, and wearable components of the mHealth platform by emulating real-world attacks and performing systematic penetration tests on all three layers of security. Each layer was examined using tools and methods specifically designed to assess the server, mobile, and wearable technologies. This analysis uncovered exploitable configurations, insecure Bluetooth Low Energy (BLE) communications, and leaks of Personal Health Records (PHR). The findings found systemic weaknesses including incomplete isolation of a container, leak of BLE meta-data, and the perpetual abuse of privacy permissions by Android applications. This study proposes practical recommendations, for developers and system architects, to help secure mHealth platform services. This study provides reproducible audit methodology, as well as a case study that is realized in real-world operational deployments, thus closing a gap between theorized models and practical security in healthcare.