Cyber Physical Systems (CPS)-Internet of Things (IoT) enabled healthcare services and infrastructures improve human life, but are vulnerable to a variety of emerging cyber-attacks. Cybersecurity specialists are finding it hard to keep pace of the increasingly sophisticated attack methods. There is a critical need for innovative cognitive cybersecurity for CPS-IoT enabled healthcare ecosystem. This paper presents a cognitive cybersecurity framework for simulating the human cognitive behaviour to anticipate and respond to new and emerging cybersecurity and privacy threats to CPS-IoT and critical infrastructure systems. It includes the conceptualisation and description of a layered architecture which combines Artificial Intelligence, cognitive methods and innovative security mechanisms.

In recent years, the cybersecurity attacks on the internet of things (IoT)-based healthcare systems became the major concern for researchers and health organizations. With time, the sophistication of these attacks is increasing. Therefore, healthcare service providers must implement efficient security mechanisms carefully while taking the advantages of connected devices without compromising the patient safety and disturbing the real-time health services. A digital twin (DT) is a virtual representation of a real-time counterpart of a physical world. DT offers significant advantages to cybersecurity experts, empowering them to predict risks without entering the physical world, and to simulate and test cyber-attacks that would otherwise be infeasible to do in real-time in the physical environment. DT in healthcare helps to identify security vulnerabilities, conduct attack simulations, and potential security breaches by creating a virtual replica of the targeted healthcare systems. In this paper, a novel and automated conceptual framework is developed for reinforcing the cybersecurity in IoT-based healthcare using DT technology. It includes the conceptualization and analysis of the proposed framework which can provide dynamic and adaptive security solution to identify real-time threats and vulnerabilities in IoTbased healthcare applications.

This paper presents a methodology for assessing security, privacy and dependability (SPD) of embedded systems. The methodology, developed through the European collaboration SHIELD, is applied for the smart grid network as deployed in the South of Norway. Three Smart Grid use cases are analysed in detail, being billing, home control and alarm. The SHIELD methodology uses a Multi-Metrics approach to evaluate the system SPD level during running processes and compares it with use case goals for S, P, and D. The simplicity, applicability, and scalability of the suggested Multi-Metrics approach is demonstrated in this paper. It shows that a single configuration is not sufficient to satisfy the given goals for all use cases.