Lightweight Graphical Visualization for Potential Threat Identification in IoT

Industrial IoT is revolutionizing traditional industrial control systems with advanced automation, real-time monitoring, and remote operation. However, its rapid adoption expands the attack surface, particularly when legacy infrastructures lacking built-in security features are integrated into highly interconnected networks. These conditions foster hybrid cyber threats, attackers increasingly employ sophisticated methods. Identifying potential intrusion pathways demands systematic modeling of both network and physical topologies, while frameworks such as MITRE ATT&CK<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> aid in mapping observed behaviors to known tactics. Graph-based attack models offer a powerful way to visualize threats, yet many existing solutions focus on theoretical methodologies rather than practical deployment, often requiring specialized expertise. This paper introduces a lightweight, one-stop framework that consolidates network modeling, vulnerability-to-attack mapping, and attack-graph generation—lowering the implementation barriers and enhancing industrial IoT security analysis against emerging hybrid cyber attacks.