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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References
Cui, J., Ma, L., Wang, R., Liu, M.: Research and optimization of GPSR routing protocol for vehicular ad-hoc network. China Commun. 19(10), 194–206 (2022)
Zhang, Y., Cui, J., Liu, M.: Research on adversarial patch attack defense method for traffic sign detection. In: Lu, W., Zhang, Y., Wen, W., Yan, H., Li, C. (eds.) Cyber Security: 19th China Annual Conference, CNCERT 2022, Beijing, China, August 16–17, 2022, Revised Selected Papers, pp. 199–210. Springer, Singapore (2022). https://doi.org/10.1007/978-981-19-8285-9_15
Liu, M., et al.: Modeling and analysis of the decentralized interactive cyber defense approach. China Commun. 19(10), 116–128 (2022)
Kang, M.J., Kang, J.W.: Intrusion detection system using deep neural network for in-vehicle network security. PLoS ONE 11(6), e0155781 (2016)
Qu, F., Wu, Z., Wang, F.Y., Cho, W.: A security and privacy review of vanets. IEEE Trans. Intell. Transp. Syst. 16(6), 2985–2996 (2015)
Zhang, Y., Li, X., Gao, L., Wang, L., Wen, L.: Imbalanced data fault diagnosis of rotating machinery using synthetic oversampling and feature learning. J. Manuf. Syst. 48, 34–50 (2018)
He, H., Yang, B., Garcia, E., Li, S.A.: Adaptive synthetic sampling approach for imbalanced learning. In: Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Hong Kong (2008)
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: an overview. IEEE Signal Process. Mag. 35(1), 53–65 (2018)
Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)
Wang, Z., She, Q., Ward, T.E.: Generative adversarial networks in computer vision: a survey and taxonomy. ACM Comput. Surv. 54(2), 1–38 (2021)
Yu, X., Cui, J., Liu, M.: An embedding carrier-free steganography method based on Wasserstein GAN. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds.) Algorithms and Architectures for Parallel Processing (ICA3PP 2021). LNCS, vol. 13156. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-95388-1_35
She, X., Sekiya, Y.: A convolutional autoencoder based method with smote for cyber intrusion detection. In: 2021 IEEE International Conference on Big Data (Big Data), pp. 2565–2573. IEEE (2021)
Soltanzadeh, P., Hashemzadeh, M.: Rcsmote: range-controlled synthetic minority over-sampling technique for handling the class imbalance problem. Inf. Sci. 542, 92–111 (2021)
Zhang, Y., Li, P., Wang, X.: Intrusion detection for iot based on improved genetic algorithm and deep belief network. IEEE Access 7, 31711–31722 (2019)
Tanaka, F.H.K.d.S., Aranha, C.: Data augmentation using gans. arXiv preprint arXiv:1904.09135 (2019)
Liu, J., Wu, N., Qiao, Y., Li, Z.: Short-term traffic flow forecasting using ensemble approach based on deep belief networks. IEEE Trans. Intell. Transp. Syst. 23(1), 404–417 (2020)
Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp 1, 108–116 (2018)
Frid-Adar, M., Klang, E., Amitai, M., Goldberger, J., Greenspan, H.: Synthetic data augmentation using gan for improved liver lesion classification. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 289–293. IEEE (2018)
Sohn, I.: Deep belief network based intrusion detection techniques: a survey. Expert Syst. Appl. 167, 114170 (2021)
Gao, N., Gao, L., Gao, Q., Wang, H.: An intrusion detection model based on deep belief networks. In: 2014 Second International Conference on Advanced Cloud and Big Data, pp. 247–252. IEEE (2014)
Belarbi, O., Khan, A., Carnelli, P., Spyridopoulos, T.: An intrusion detection system based on deep belief networks. In: Su, C., Sakurai, K., Liu, F. (eds.) Science of Cyber Security: 4th International Conference, SciSec 2022, Matsue, 10–12 August 2022, Revised Selected Papers, pp. 377–392. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-17551-0_25
Roopak, M., Tian, G.Y., Chambers, J.: Deep learning models for cyber security in IoT networks. In: 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0452–0457. IEEE (2019)
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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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