Wireless Sensor Networks (WSN) are commonly adopted by the Industrial Internet of Things (IIoT) scenarios due to their easy and fast deployment. However, WSNs are especially vulnerable to security attacks as reflexive packet flooding Denial of Service (DoS) may occur. Admission control and reputation-based strategies are effective for attacks detection. However, mitigation strategies to deal with the effects of these attacks, such as congestion channel transmission is an open issue. Additionally, the resource-constrained nature of nodes, as low bandwidth, limited memory, and battery within WSNs, poses a challenge to develop efficient mechanisms in such scenario. To address this issue, we propose a Distributed Congestion Control by Duty-cycle Restriction (D-ConCReCT) to detect and mitigate DoS in IIoT. The main goal is to investigate its feasibility in large-scale networks, as well as its ability to reduce both the detection and mitigation times when compared to a previous centralized approach, so-called ConCReCT. Our results indicate that D-ConCReCT may be applied in the mitigation of DoS attacks in a sensor network scenario with 500 nodes.

Healthcare 5.0 represents the next evolution in intelligent and interconnected healthcare systems, leveraging emerging technologies such as Artificial Intelligence (AI) and the Internet of Medical Things (IoMT) to enhance patient care and automation. While Intrusion Detection Systems (IDSs) are a critical component for securing these environments, the current literature lacks a systematic analysis that jointly evaluates the effectiveness of AI models, the suitability of datasets, and the role of Explainable Artificial Intelligence (XAI) in the Healthcare 5.0 landscape. To fill this gap, this survey provides a comprehensive review of IDSs for Healthcare 5.0, analyzing state-of-the-art approaches and available datasets. Furthermore, a practical case study is presented, demonstrating that the fusion of network and biomedical features significantly improves threat detection, with physiological signals proving crucial for identifying complex attacks like spoofing. The primary contribution is therefore an integrated analysis that bridges the gap between cybersecurity theory and clinical practice, offering a guide for researchers and practitioners aiming to develop more secure, transparent, and patient-centric systems.

Healthcare 5.0 integrates Artificial Intelligence (AI), the Internet of Things (IoT), real-time monitoring, and human-centered design toward personalized medicine and predictive diagnostics. However, the increasing reliance on interconnected medical technologies exposes them to cyber threats. Meanwhile, current AI-driven cybersecurity models often neglect biomedical data, limiting their effectiveness and interpretability. This study addresses this gap by applying eXplainable AI (XAI) to a Healthcare 5.0 dataset that integrates network traffic and biomedical sensor data. Classification outputs indicate that XGBoost achieved 99% F1-score for benign and data alteration, and 81% for spoofing. Explainability findings reveal that network data play a dominant role in intrusion detection whereas biomedical features contributed to spoofing detection, with temperature reaching a Shapley values magnitude of 0.37.

Security in wireless sensor networks (WSNs) demands efficient key management schemes. In particular, one of the main research challenges is to ensure secure key storage by sensors due to their constrained resources and high exposure to tampering attempts. To address this issue, we have proposed SENSORLock, a secure key storage mechanism which can be applied to different key distribution architectures for WSNs. In this work, we evaluate SENSORLock through different study cases considering three key distribution architectures: TinySec, SPINS, and NCD. Our goal is to demonstrate its feasibility in large-scale sensor networks. Simulation results indicate that our mechanism introduces an average overhead of 1.9% in terms of end to end delay and provides a similar estimated power consumption compared to the aforementioned architectures. Hence, we argue that it is feasible to use SENSORLock (i) in large-scale sensor networks and (ii) under different key distribution schemes.