A deep learning approach to secure vehicle to road side unit communications in intelligent transportation system. (January 2023)

• Record Type: Journal Article
• Authors: Wu, Yun; Wu, Liangshun; Cai, Hengjin
• Is Part Of: Computers & electrical engineering. Volume 105(2023)
• Publication Date: 2023-01
• Subjects: ITS -- V2R -- Communication security -- Deep learning; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108542
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Encryption technology alone cannot verify the authenticity of the messages. Complete cloud-vehicle-road architecture that apply to multiple scenarios. A binary classification model to assess uploaded traffic-related messages. A novel graph temporal neural network with attention to detect misbehavior of vehicles. Abstract: Vehicle and road side unit communications are crucial to the information network in Intelligent Transportation System (ITS). There are two fundamental problems with the current communication security solutions: (1) encryption technology alone is not sufficient to verify the authenticity of the messages transmitted from vehicles to road side units, (2) existing solutions fail to build a complete framework in the way that they are case-dependent and apply to limited scenarios. This paper first presents a cloud-vehicle-road architecture that explains the precise message contents as well as the message generation and transmission process. A binary classification model is deployed to assess uploaded traffic-related messages to improve traffic efficiency based on cross-sectional data. A novel graph temporal neural network with attention is designed for misbehavior detection of vehicles based on time-sequential data. According to simulation results, the system's overall performance can be effectively improved regarding security and availability. Graphical abstract: Image, graphical abstract