Intelligent Security Authentication for Connected and Autonomous Vehicles: Attacks and Defenses

• Published in: Electronics (Basel) Vol. 13; no. 8; p. 1577
• Main Authors: Qiu, Xiaoying, Yu, Jinwei, Jiang, Wenbao, Sun, Xuan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2024
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Authentication; Autonomous vehicles; Biometry; Collaboration; Control systems; Datasets; Deep learning; Design; Discriminators; Driverless cars; Generative adversarial networks; Innovations; Machine learning; Methods; Military vehicle industry; Neural networks; Security; Spoofing; Vehicles; Wireless communications
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics13081577

The emergence of integrated positioning, communication, and sensing technologies has paved the way for a surge in connected and autonomous vehicles. The control system has been successful in reliable and fast transmission. However, practical applications face security risks, especially data tampering and spoofing attacks. To improve the resilience of the system against potential attacks, we attempt to leverage a generative adversarial network learning-assisted authentication framework (GAF). In addition to proposing a new method for validating vehicles, we also introduce a new architectural innovation in the generator–discriminator pair to achieve improved results. The generator sub-network is constructed using an advanced convolutional neural network, whereas the discriminator is designed to leverage global and local information to determine whether a signal is real or fake. On this basis, we propose a signal enhancement-based authentication method, a deep convolutional generative adversarial network (DCGAN). Experimental results using the National Institute of Standards and Technology (NIST) dataset show that the proposed method is effective in denoising and improving the detection performance.