Abstract
At present, cyber attacks on vehicle network have are proliferating, one of the most significant difficulties in the current detection methods is that the malicious flows are small and discrete in the whole link. In view of the above issues, this paper proposed a detection model based on the integration of Generative Adversarial Networks (GANs) and Deep Belief Networks (DBN). In this model, GANs is first used to enhance the few malicious flow samples, and then an improved DBN is used to evaluate the effect of data generation, so as to improve the uneven distribution of samples in the data set. In the testing section, open data set CIC-IDS2017 was selected for data enhancement and evaluated the performance of the proposed model. The experimental results show that the proposed model has significantly improved the detection performance of few cyber attacks samples compared with traditional detection algorithms. In addition, compared with the method of merge-generate data set approach, the accuracy rate, recall rate, F1 value and other evaluation indexes of the proposed model for the few samples detection have been greatly improved. Therefore, it can be considered that the proposed model is effective than current methods in dealing with the uneven distribution of data sets in traditional cyber attack detection.
This work is financially supported by the National Natural Science Foundation of China under Grant 62106060.
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Duan, Y., Cui, J., Jia, Y., Liu, M. (2024). Intrusion Detection Method for Networked Vehicles Based on Data-Enhanced DBN. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14488. Springer, Singapore. https://doi.org/10.1007/978-981-97-0801-7_3
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