Privacy-Preserving Mechanism for Vehicle-To-Everything (V2X) Technologies: Hybridization of Deep Learning with Optimization Algorithms for Secure Smart Transportation in Dubai

  • Authors

    • Dr Fadi Sakka HR Mark Consultancy Research and Development
    https://doi.org/10.14419/tnkyex43

    Received date: September 10, 2025

    Accepted date: October 21, 2025

    Published date: November 2, 2025

  • Vehicle-to-Everything; Internet of Vehicles; Traffic Systems; Artificial Intelligence; Network Security; Privacy-Preserving

  • Abstract

    The progress of artificial intelligence (AI) and Internet of Things (IoT) technologies has resulted in a steady upsurge in the intellect and network abilities of vehicles. As an outcome, the IoT-based vehicle-to-everything (V2X) interaction methods are also recognized as the Internet of Vehicles (IoV). The IoV has garnered significant attention from both industry and academia. Whereas an inter-vehicular system links an automobile to exterior devices utilizing the technology of V2X. To reduce accidents of smart vehicles and detect malicious assaults in vehicular systems, numerous scholars have performed machine learning (ML)-based techniques for intrusion detection in IoT environments. In this study, we focus on the design and implementation of Privacy-Preserving Vehicle-to-Everything Technologies using Hybrid Deep Learning and Optimization Algorithms (PPV2XT-HDLOA) for Smart Transportation in Dubai. The presented PPV2XT-HDLOA model enhances V2X transportation by leveraging advanced data-driven techniques to optimize vehicle communication. To achieve this, the PPV2XT-HDLOA model applies the z-score normalization approach for data normalization to ensure data uniformity and enhance model convergence. To reduce dimensionality, the reptile search algorithm (RSA) can be employed to recognize the most relevant features. For the classification process, the hybrid deep learning model combining bidirectional temporal convolutional networks and bidirectional gated recurrent units (BiTCN-BiGRU) technique is exploited. Finally, the hyperparameter tuning of the BiTCN-BiGRU technique is carried out using the mountain gazelle optimization (MGO) algorithm to achieve optimal fine-tuning of parameters, ensuring superior classification performance. To demonstrate the better solution of the PPV2XT-HDLOA technique, a wide range of simulations have been tested, and the outcomes are inspected under several measures. The comparison investigation reported the improvement of the PPV2XT-HDLOA technique under various metrics.

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  • How to Cite

    Sakka, D. F. . (2025). Privacy-Preserving Mechanism for Vehicle-To-Everything (V2X) Technologies: Hybridization of Deep Learning with Optimization Algorithms for Secure Smart Transportation in Dubai. International Journal of Basic and Applied Sciences, 14(7), 46-57. https://doi.org/10.14419/tnkyex43