This work introduces the design, architecture, implementation, and testing of a low-cost and machine-learning-enabled device to be worn on the wrist. The suggested wearable device has been developed for use during emergency incidents of large passenger ship evacuations, and enables the real-time monitoring of the passengers’ physiological state, and stress detection. Based on a properly preprocessed PPG signal, the device provides essential biometric data (pulse rate and oxygen saturation level) and an efficient unimodal machine learning pipeline. The stress detecting machine learning pipeline is based on ultra-short-term pulse rate variability, and has been successfully integrated into the microcontroller of the developed embedded device. As a result, the presented smart wristband is able to provide real-time stress detection. The stress detection system has been trained with the use of the publicly available WESAD dataset, and its performance has been tested through a two-stage process. Initially, evaluation of the lightweight machine learning pipeline on a previously unseen subset of the WESAD dataset was performed, reaching an accuracy score equal to 91%. Subsequently, external validation was conducted, through a dedicated laboratory study of 15 volunteers subjected to well-acknowledged cognitive stressors while wearing the smart wristband, which yielded an accuracy score equal to 76%.

This paper provides an overview of the Advanced Threat Intelligence Orchestrator in assisting organizations and society's first responders in managing, prioritizing, and sharing information related to cyber security incidents. In order to accomplish this, the capabilities and benefits of security, orchestration, automation, and response (SOAR) systems, on which Orchestrator is based, were promoted. The results of this survey conducted as part of the IRIS EU-funded project to protect Internet of Things (IoT) and Artificial Intelligence (AI)-driven ICT-enabled systems from cyber threats and attacks on their privacy facilitating SOC/CSIRTs/CERTs. In this context, the tool is explored in methods of orchestrating and automating cyber security processes and routines. The open-source tool that was chosen for the creation of Advanced Threat Intelligence Orchestrator was SHUFFLE. SHUFFLE gives a wide variety of functionalities as it can be integrated with numerous tools and APIS. Furthermore, the provision of schematic workflows with action steps makes the stakeholders' interface more intuitive.