Digital-Twin-Based Security Analytics for the Internet of Things

Abstract

Although there are numerous advantages of the IoT in industrial use, there are also some security problems, such as insecure supply chains or vulnerabilities. These lead to a threatening security posture in organizations. Security analytics is a collection of capabilities and technologies systematically processing and analyzing data to detect or predict threats and imminent incidents. As digital twins improve knowledge generation and sharing, they are an ideal foundation for security analytics in the IoT. Digital twins map physical assets to their respective virtual counterparts along the lifecycle. They leverage the connection between the physical and virtual environments and manage semantics, i.e., ontologies, functional relationships, and behavioral models. This paper presents the DT2SA model that aligns security analytics with digital twins to generate shareable cybersecurity knowledge. The model relies on a formal model resulting from previously defined requirements. We validated the DT2SA model with a microservice architecture called Twinsight, which is publicly available, open-source, and based on a real industry project. The results highlight challenges and strategies for leveraging cybersecurity knowledge in IoT using digital twins.