An Efficient Cybersecurity Spoofing Attacks Detection‎Framework in Vehicular Networks Using Hybridized ‎Dilated and Attention-Based Network

  • Authors

    https://doi.org/10.14419/m4s0q161

    Received date: July 14, 2025

    Accepted date: August 24, 2025

    Published date: September 4, 2025

  • Cybersecurity Spoofing Attack Detection; Vehicular Network; Fitness Entrenched Coronavirus Mask Protection Algorithm; ‎Restricted Boltzmann Machine; Hybrid Dilated and Attention-Based Network; Deep Temporal Convolution Network; Residual ‎Long Short Term Memory network.

  • Abstract

    Due to the growing cybersecurity threats, the privacy and security of modern vehicles have become increasingly significant. ‎Cyber offenses in vehicular networks are recognized using an Intrusion Detection System (IDS). The rapid growth of multimedia technologies has made cybersecurity an essential concern.. Automated and internet-connected cars are exposed to spoofing ‎and jammer attacks. GPS location spoofing is an imminent danger to Connected and Autonomous Vehicles (CAV), threatening ‎security and even exposing motorists and pedestrians to risk. Because of flawed protocol design and increased interconnectivity ‎among modern autonomous vehicles, the Controller Area Network (CAN) bus is insecure. The identification of spoofing ‎attacks on the CAN bus is critical. Hence, it is necessary to develop an efficient spoofing attack detection method to address the ‎limitations of existing models. The major phases of the developed framework are (a) Data Collection, (b) Data Pre-processing, ‎‎(c) Weighted Feature Selection, and (d) Attack Detection. At first, essential data utilized for the validation is collected in the ‎standard dataset. Further, the gathered time series data is given as input to the data pre-processing phase. Later, the attained pre-‎processed data is utilized to collect the essential features. Further, in the weighted feature selection phase, the Restricted Boltz-‎mann Machine (RBM) technique is utilized to attain the significant features. Unlike the conventional CMPA, the proposed FE-CMPA introduces a fitness-entrenchment mechanism that improves the optimization of RBM feature weights and enhances ‎relief score maximization. Subsequently, the acquired weighted RBM features are provided for the attack detection phase. Fur-‎Furthermore, the spoofing attacks are detected using the developed Hybrid Dilated and Attention-based Network (HD-ANet), ‎which holds the Deep Temporal Convolution Network (DTCN) and Residual Long Short Term Memory (RLSTM) network ‎for effective validation. Hence, the implemented spoofing attack detection model is more secure and achieves a comparatively ‎higher detection rate than traditional approaches in various experimental evaluations.

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    Deshmukh, V. R. ., & Borse, D. I. S. . (2025). An Efficient Cybersecurity Spoofing Attacks Detection‎Framework in Vehicular Networks Using Hybridized ‎Dilated and Attention-Based Network. International Journal of Basic and Applied Sciences, 14(5), 114-130. https://doi.org/10.14419/m4s0q161