  In the present era of advanced technology, the Internet of Things (IoT) plays
a crucial role in enabling smart connected environments. This includes various
domains such as smart homes, smart healthcare, smart cities, smart vehicles,
and many others.With ubiquitous smart connected devices and systems, a large
amount 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, support
the development of anomaly detection models for the Vehicular Internet of
Things (V-IoT), which encompasses collaborative automatic driving and enhanced
transportation systems. However, traditional centralized anomaly detection
models fail to provide better services for connected vehicles due to issues
such as high latency, privacy leakage, performance overhead, and model drift.
Recently, Federated Learning (FL) has gained significant recognition for its
ability to address data privacy concerns in the IoT domain. Digital Twin (DT),
proves beneficial in addressing uncertain crises and data security issues by
creating a virtual replica that simulates various factors, including traffic
trajectories, city policies, and vehicle utilization. However, the
effectiveness of a V-IoT DT system heavily relies on the collection of
long-term and high-quality data to make appropriate decisions. This paper
introduces a Hierarchical Federated Learning (HFL) based anomaly detection
model for V-IoT, aiming to enhance the accuracy of the model. Our proposed
model integrates both DT and HFL approaches to create a comprehensive system
for detecting malicious activities using an anomaly detection model.
Additionally, real-world V-IoT use case scenarios are presented to demonstrate
the application of the proposed model.
