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Communication Papers of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 37

To propose an attack detection model for enhancing the security of 5G-enabled Vehicle-to-Everything (V2X) communication for smart vehicle

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DOI: http://dx.doi.org/10.15439/2023F7757

Citation: Communication Papers of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 37, pages 235–242 ()

Full text

Abstract. The evolution of 5G technology has revolutionized the communication landscape, enabling faster speeds, low latency, and increased capacity. The integration of 5G technology and the emergence of the 5G-enabled V2X communication network are driving the transformation of the automotive industry. Connected cars and the software-defined vehicle concept enable new business models and enhanced safety measures. However, ensuring the security of the 5G-enabled V2X communication network is crucial to mitigate potential attacks and protect the integrity of the ecosystem. To identify this potential attack, we have proposed a novel deep learning-based attack detection model (ADM) for detecting attack in 5G-enabled V2X communication network. In this we have used correlation coefficient as the feature selection method and used the deep learning-based stacked LSTM model for attack detection. The performance metrices are detection rate, accuracy, precision and F1-score

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