ArmAssist (AA), developed by TECNALIA, is a telerehabilitation platform aiming to help poststroke subjects maintain the rehabilitation of the upper limb at home. AA includes robotic modules with multiple sensors to train and measure the users’ voluntary movements. An assessment platform based on serious games is also included to not only engage the user but also perform automated evaluations of arm and hand function and their evolution over time. Moreover, AA facilitates at-home rehabilitation with limited remote supervision by the therapist. In the present article, the technical specifications and developments of AA are described. Additionally, a summary of the outcomes of a usability evaluation of AA is presented.

There is a growing concern about the dynamic landscape of cyber security threats escalating, and the need for improvement in defence capabilities against emerging sophisticated incidents. In response, this paper presents a solution called the Cyber Incident Simulation System, which enables system security engineers to simulate cyber-physical attacks and incidents without the requirement to affect or disrupt the ongoing business operation of the system. Leveraging graph-based threat modelling and AI-generated incident data, the system empowers professionals to predict the effect of the incident within the system under study. The synthetic data is used by anomaly-based Intrusion Detection Systems (IDSs) and other additional security controls to improve their detection algorithms to enhance their accuracy and effectiveness. The Cyber Incident Simulation System is designed to enhance the cyber security measures through the simulation of various incident scenarios.

In the context of cybersecurity digital twin environments, the ability to simulate realistic network traffic is critical for validating and training intrusion detection systems. However, generating synthetic data that accurately reflects the complex, time-dependent nature of network flows remains a significant challenge. This paper presents an AI-based data generation approach designed to generate multivariate temporal network flow data that accurately reflects adversarial scenarios. The proposed method integrates a Long Short-Term Memory (LSTM) architecture trained to capture the temporal dynamics of both normal and attack traffic, ensuring the synthetic data preserves realistic, sequence-aware behavioral patterns. To further enhance data fidelity, a combination of deep learning-based generative models and statistical techniques is employed to synthesize both numerical and categorical features while maintaining the correct proportions and temporal relationships between attack and normal traffic. A key contribution of the framework is its ability to generate high-fidelity synthetic data that supports the simulation of realistic, production-like cybersecurity scenarios. Experimental results demonstrate the effectiveness of the approach in generating data that supports robust machine learning-based detection systems, making it a valuable tool for cybersecurity validation and training in digital twin environments.

This chapter introduces SIM4BC, a scalable and modular framework designed to generate high-fidelity synthetic data for enhancing and evaluating cybersecurity threat detection and response in complex cyber-physical systems. The framework combines graph-based threat modeling with AI-driven data generation, enabling the creation of realistic attack scenarios that preserve both temporal dynamics and statistical properties of real-world threats. A key innovation lies in its decoupled architecture, which separates temporal sequence modeling from static feature synthesis, allowing independent optimization of sequential and structural components. This design supports dynamic attack simulation by enabling users to define custom TTPs (Tactics, Techniques, and Procedures) and inject synthetic adversarial patterns into baseline network traffic. Validation through TSTR (Train-on-Synthetic, Test-on-Real) and TRTS (Train-on-Real, Test-on-Synthetic) methodologies demonstrates robust performance, with intrusion detection systems achieving up to 96% accuracy when tested on synthetic data. By combining architectural analysis with simulated attack data, SIM4BC offers a balanced and scalable approach for threat modeling and resilience assessment, enabling advanced secure-by-design practices and improving the reliability of cybersecurity defences.