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In the present era of advanced technology, the Internet of Things (IoT) playsa crucial role in enabling smart connected environments. This includes variousdomains such as smart homes, smart healthcare, smart cities, smart vehicles,and many others.With ubiquitous smart connected devices and systems, a largeamount of data associated with them is at a prime risk from malicious entities(e.g., users, devices, applications) in these systems. Innovative technologies,including cloud computing, Machine Learning (ML), and data analytics, supportthe development of anomaly detection models for the Vehicular Internet ofThings (V-IoT), which encompasses collaborative automatic driving and enhancedtransportation systems. However, traditional centralized anomaly detectionmodels fail to provide better services for connected vehicles due to issuessuch as high latency, privacy leakage, performance overhead, and model drift.Recently, Federated Learning (FL) has gained significant recognition for itsability to address data privacy concerns in the IoT domain. Digital Twin (DT),proves beneficial in addressing uncertain crises and data security issues bycreating a virtual replica that simulates various factors, including traffictrajectories, city policies, and vehicle utilization. However, theeffectiveness of a V-IoT DT system heavily relies on the collection oflong-term and high-quality data to make appropriate decisions. This paperintroduces a Hierarchical Federated Learning (HFL) based anomaly detectionmodel for V-IoT, aiming to enhance the accuracy of the model. Our proposedmodel integrates both DT and HFL approaches to create a comprehensive systemfor detecting malicious activities using an anomaly detection model.Additionally, real-world V-IoT use case scenarios are presented to demonstratethe application of the proposed model.

Integration of Digital Twin and Federated Learning for Securing Vehicular Internet of Things