A Review on Attack Graph Analysis for IoT Vulnerability Assessment: Challenges, Open Issues, and Future Directions

Vulnerability assessment in industrial IoT networks is critical due to the evolving nature of domain and increasing complexity security threats. This study aims address existing gaps literature by conducting a comprehensive survey on use attack graphs for vulnerability networks. Attack serve as valuable cybersecurity tool modeling analyzing potential scenarios systems, networks, or applications. The covers research conducted between 2016 2021(34 peer-reviewed journal articles 28 conference papers), identifying categorizing main methodologies technologies employed generating graphs. In this review, core techniques are highlighted, such Markov Decision Processes (MDP), Feature Pyramid Networks (FPN), K-means clustering, logistic regression models, along with other involving genetic algorithms like fast-forward (FF), contingent fast-forwards (CFF), advanced reinforcement-learning algorithms, HARMs models. evaluation performance these graph models using devices case studies also emphasized. provides insights into state-of-the-art network assessment, various applications, performances, opportunities, challenges. As reference source, it serves inform academicians practitioners interested leveraging guides future directions area.