Abstract
The rapid development of the automotive Industrial Internet of Things requires secure networking infrastructure toward digitalization. Cybertwin (CT) is a next-generation networking architecture that serves as a communication, and digital asset owner, and can make the Vehicle-to-Everything (V2X) network flexible and secure. However, CT itself can publish end users' digital assets to other entities as a service, making data security and privacy major obstacles in the realization of V2X applications. Motivated from the aforementioned discussion, this article presents BDTwin, a blockchain and deep-learning-based integrated framework to enhance security and privacy in CT-driven V2X applications. Specifically, a blockchain scheme is designed to ensure secure communication among vehicles, roadside units, CT-edge server, and cloud server using a smart contract-based enhance-Proof-of-Work (ePoW) and Zero Knowledge Proof (ZKP)-based verification process. Smart contracts are used to enforce rules and regulations that govern the behavior of V2X entities in a nondeniable and automated manner. In a deep-learning scheme, an autoregressive-deep variational autoencoder model is combined with attention-based bidirectional long short-term memory (A-BLSTM) for automatic feature extraction and attack detection by analyzing CT-edge servers data in a V2X environment. Security analysis and experimental results using two different sources, ToN-IoT and CICIDS-2017 show the superiority of the proposed BDTwin framework over some baseline and recent state-of-the-art techniques.