Integration of Digital Twin and Federated Learning for Securing Vehicular Internet of Things
Deepti Gupta, Shafika Showkat Moni, Ali Şaman Tosun
Abstract
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. The IoT facilitates the integration and interconnection of devices, enabling them to communicate, share data, and work together to create intelligent and efficient systems. 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.