VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security
Vehicle-to-everything (V2X) communications enable vehicles to interact with each other and various components of the traffic system, forming the backbone of modern intelligent transportation systems. However, V2X communications are highly susceptible to cyberattacks, posing a threat to both safety a...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/12/6739 |
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| author | Yonas Teweldemedhin Gebrezgiher Sekione Reward Jeremiah Stefanos Gritzalis Jong Hyuk Park |
| author_facet | Yonas Teweldemedhin Gebrezgiher Sekione Reward Jeremiah Stefanos Gritzalis Jong Hyuk Park |
| author_sort | Yonas Teweldemedhin Gebrezgiher |
| collection | DOAJ |
| description | Vehicle-to-everything (V2X) communications enable vehicles to interact with each other and various components of the traffic system, forming the backbone of modern intelligent transportation systems. However, V2X communications are highly susceptible to cyberattacks, posing a threat to both safety and operational efficiency. This paper proposes a real-time anomaly detection framework that integrates the reconstruction capabilities of Variational Autoencoders (VAEs) with the feature extraction power of Convolutional Neural Networks (CNNs). Our model processes streaming data using a sliding window mechanism, ensuring prompt detection of anomalies in the dynamic V2X environment. Extensive experiments demonstrate that our method achieves high performance across diverse anomaly types, with precision, recall, and F1-scores reaching up to 0.91, 0.99, and 0.95, respectively, on challenging anomalies such as constant position offsets. The model consistently outperforms both a traditional autoencoder and a VAE with Long Short-Term Memory (LSTM) layers, particularly on complex anomalies like vehicle speed and position offsets. Additionally, our framework maintains a low inference time of approximately 0.0013 s, making it highly suitable for real-time deployment. Designed to adapt to evolving traffic patterns through periodic retraining, the proposed approach ensures long-term reliability and robustness. By delivering high performance, adaptability, and efficiency, our method provides a reliable way to detect and prevent cyberattacks, thereby making intelligent transportation systems safer and more dependable. |
| format | Article |
| id | doaj-art-e5b767020a354691a1841acaa4ad1095 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-e5b767020a354691a1841acaa4ad10952025-08-20T03:26:49ZengMDPI AGApplied Sciences2076-34172025-06-011512673910.3390/app15126739VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication SecurityYonas Teweldemedhin Gebrezgiher0Sekione Reward Jeremiah1Stefanos Gritzalis2Jong Hyuk Park3Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of KoreaDepartment of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of KoreaDepartment of Digital Systems, University of Piraeus, 18532 Piraeus, GreeceDepartment of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of KoreaVehicle-to-everything (V2X) communications enable vehicles to interact with each other and various components of the traffic system, forming the backbone of modern intelligent transportation systems. However, V2X communications are highly susceptible to cyberattacks, posing a threat to both safety and operational efficiency. This paper proposes a real-time anomaly detection framework that integrates the reconstruction capabilities of Variational Autoencoders (VAEs) with the feature extraction power of Convolutional Neural Networks (CNNs). Our model processes streaming data using a sliding window mechanism, ensuring prompt detection of anomalies in the dynamic V2X environment. Extensive experiments demonstrate that our method achieves high performance across diverse anomaly types, with precision, recall, and F1-scores reaching up to 0.91, 0.99, and 0.95, respectively, on challenging anomalies such as constant position offsets. The model consistently outperforms both a traditional autoencoder and a VAE with Long Short-Term Memory (LSTM) layers, particularly on complex anomalies like vehicle speed and position offsets. Additionally, our framework maintains a low inference time of approximately 0.0013 s, making it highly suitable for real-time deployment. Designed to adapt to evolving traffic patterns through periodic retraining, the proposed approach ensures long-term reliability and robustness. By delivering high performance, adaptability, and efficiency, our method provides a reliable way to detect and prevent cyberattacks, thereby making intelligent transportation systems safer and more dependable.https://www.mdpi.com/2076-3417/15/12/6739V2X securitygenerative AIVAECNNanomaly detectionGAN |
| spellingShingle | Yonas Teweldemedhin Gebrezgiher Sekione Reward Jeremiah Stefanos Gritzalis Jong Hyuk Park VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security Applied Sciences V2X security generative AI VAE CNN anomaly detection GAN |
| title | VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security |
| title_full | VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security |
| title_fullStr | VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security |
| title_full_unstemmed | VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security |
| title_short | VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security |
| title_sort | vae based real time anomaly detection approach for enhanced v2x communication security |
| topic | V2X security generative AI VAE CNN anomaly detection GAN |
| url | https://www.mdpi.com/2076-3417/15/12/6739 |
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