Connected and autonomous vehicles (CAVs) promise improved safety, efficiency, and convenience but are vulnerable to adversarial attacks due to complex networks. This paper introduces a deep learning (DL) methodology integrating graph neural networks (GNNs) and Variational Autoencoders (VAEs). GNNs capture network relationships, learning robust node embeddings, while VAEs model latent space and reconstruction errors for probabilistic anomaly detection. This approach creates a graph from CAV data, extracts feature with GNNs, and uses VAEs to detect anomalies, forming a robust, updatable architecture to counter emerging threats and mitigate attack vectors. Evaluated on car-hacking datasets, the proposed method improves precision by 3.43%, recall by 2.94%, F-measure by 2.9%, training accuracy by 0.78%, and testing accuracy by 1.09%, with lower model loss, ensuring safer and more resilient autonomous vehicle operations.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Robust Adversarial Attack Detection in Connected and Autonomous Vehicle Networks Using GNN and Variational Autoencoders


    Additional title:

    Lect. Notes in Networks, Syst.



    Conference:

    International Conference on Data Engineering and Communication Technology ; 2024 ; Kuala Lumpur, Malaysia September 28, 2024 - September 29, 2024



    Publication date :

    2025-07-12


    Size :

    14 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Certifiably Robust Variational Autoencoders

    Barrett, Ben / Camuto, Alexander / Willetts, Matthew et al. | ArXiv | 2021

    Free access

    Anomaly Detection in Autonomous Vehicle’s Lidar Sensor Data Using Variational Autoencoders

    Sboui, Nourhane / Hadded, Mohamed / Ghazzai, Hakim et al. | IEEE | 2024




    Deep Tracking Portfolios Using Autoencoders and Variational Autoencoders

    Urrego, Daniel Aragón / Nieto, Oscar Eduardo Reyes / Quimbayo, Carlos Andrés Zapata | Springer Verlag | 2024