Abstract
The popularity of Connected Autonomous Vehicles (CAVs) has led to improvements in the efficiency of the transportation system. Controller Area Network (CAN) is the standard communication protocol used in CAVs. However, the absence of effective security measures within CAN has resulted in vulnerabilities that can be exploited by attackers. To address this issue, we propose an Intrusion Detection System (IDS) using Time series Imaging and Deep Learning called TIDL-IDS. First, the CAN ID in the CAN frame is encoded as the Markov Transition Field (MTF) images to take into account the temporal characteristics of the CAN time series. Given the limited resources in the vehicle network environment, a simple four-layer deep convolutional neural network is designed to classify the converted images. A comprehensive evaluation of TIDL-IDS on real datasets demonstrates that the proposed method outperforms the other two baseline methods in terms of F1 score and accuracy. Furthermore, the model parameters are also superior to those of the other methods.
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Acknowledgments
This study was funded by the Scientific research project of Hunan Provincial Department of Education under Grant 22C0229 and Graduate Research Innovation Project of Changsha University of Science and Technology (CLKYCX24063).
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Xia, Z., Huang, L., Tan, J., Jiang, F., Hu, Z. (2025). TIDL-IDS: A Time-Series Imaging and Deep Learning-Based IDS for Connected Autonomous Vehicles. In: Mouha, N., Nikiforakis, N. (eds) Information Security. ISC 2024. Lecture Notes in Computer Science, vol 15258. Springer, Cham. https://doi.org/10.1007/978-3-031-75764-8_14
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DOI: https://doi.org/10.1007/978-3-031-75764-8_14
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