Sohag Kabir

Sohag Kabir, Koorosh Aslansefat, Prosanta Gope et al.

The offshore wind energy is increasingly becoming an attractive source of energy due to having lower environmental impact. Effective operation and maintenance that ensures the maximum availability of the energy generation process using offshore facilities and minimal production cost are two key factors to improve the competitiveness of this energy source over other traditional sources of energy. Condition monitoring systems are widely used for health management of offshore wind farms to have improved operation and maintenance. Reliability of the wind farms are increasingly being evaluated to aid in the maintenance process and thereby to improve the availability of the farms. However, much of the reliability analysis is performed offline based on statistical data. In this article, we propose a drone-assisted monitoring based method for online reliability evaluation of wind turbines. A blade system of a wind turbine is used as an illustrative example to demonstrate the proposed approach.

Alhassan Abdulhamid, Sohag Kabir, Ibrahim Ghafir et al.

The Internet of Things (IoT) is integral to modern cyber-physical systems. Quantitative cybersecurity assessment in IoT environments remains challenging due to heterogeneous system architectures, evolving threat landscapes, and the limited availability of reliable probabilistic exploitability data. Although Attack Tree Analysis (ATA) provides a structured framework for modelling potential attack paths leading to system compromise, conventional ATA quantification often relies on subjective expert judgement or heuristic scoring schemes, which can introduce uncertainty and reduce analytical reproducibility. This study introduces a data-driven probabilistic security framework for IoT-based safety-critical systems by integrating Model-Based Systems Engineering (MBSE), ATA, and empirical vulnerability data. In the proposed framework, SysML models capture system architecture, from which attack trees are derived. Vulnerabilities are mapped as Basic Attack Steps and assigned exploitation probabilities using the Exploit Prediction Scoring System (EPSS). The attack tree is then represented as a Bayesian Network, enabling probabilistic reasoning, diagnostic inference, and vulnerability criticality analysis. The framework quantifies system compromise probabilities, identifies likely causes of attacks, and prioritises mitigation strategies. By combining architecture-driven modelling with real-world vulnerability intelligence, it provides a rigorous, reproducible approach for cybersecurity risk assessment in complex IoT environments.