Abstract
The bus of the Controller area Network (CAN) in the vehicle is frequently attacked under the environment of efficient communication. This paper explores ways to hide features of the intrusion detection system (IDS) and obtain a high-precision during an attack on the Internet of vehicle (IoV). To protect the privacy features of the hidden layer with regard to anomaly detection, we proposed the CVNNs-IDS. The system converts the data into an image in real-time using the encoder and then maps it into the complex domain whiles it rotates it to reconstruct the real features to achieve the purpose of system protection. Available researches show that features from random angles are obtained by attackers, making it impossible to distinguish between the real or fake feature. The accuracy of the proposed method CVNNs-IDS is 98%. Results obtained represents that our proposed method performed better than the traditional techniques with regard to performance and security.
Supported by the Innovation Plan for Postgraduate Research of Jiangsu Province in 2014 under Grant KYLX1057.
National Science Foundation of China under Grant 61902156.
Natural Science Foundation of Jiangsu Province under Grant BK20180860.
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Acknowledgment
This studied was supported by the Innovation Plan for Postgraduate Research of Jiangsu Province in 2014 under Grant KYLX1057, National Science Foundation of China under Grant 61902156 and Natural Science Foundation of Jiangsu Province under Grant BK20180860 .
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Han, M., Cheng, P., Ma, S. (2020). CVNNs-IDS: Complex-Valued Neural Network Based In-Vehicle Intrusion Detection System. In: Yu, S., Mueller, P., Qian, J. (eds) Security and Privacy in Digital Economy. SPDE 2020. Communications in Computer and Information Science, vol 1268. Springer, Singapore. https://doi.org/10.1007/978-981-15-9129-7_19
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DOI: https://doi.org/10.1007/978-981-15-9129-7_19
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