Abstract
With the development of 6G technology, security and privacy have become extremely important in the face of larger network traffic bandwidth. An effective intrusion detection system can deal with the network attacks. Deep learning has been developed in the field of intrusion detection, which can identify normal and abnormal traffic. However, existing methods cannot guarantee good performance in accuracy and efficiency. In this paper, based on the autoencoder and generative adversarial network, the multiresolution autoencoder is adopted in the network traffic feature extraction, which can obtain different encoding lengths and guarantee better data reconstruction. In addition, we add an extra feature matching loss to encourage the discriminator to get more discriminative information from the reconstructed samples. Our experimental results on the CIC-IDS2018 dataset indicates that compared with autoencoder and generative adversarial network, our model can effectively improve the detection accuracy and can be applied to 6G network traffic security detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Saad, W., Bennis, M., Chen, M.: A vision of 6G wireless systems: applications, trends, technologies, and open research problems. IEEE Network 34(3), 134–142 (2019)
De Alwis, C., Kalla, A., Pham, Q.V., et al.: Survey on 6G frontiers: trends, applications, requirements, technologies and future research. IEEE Open J. Commun. Soc. 2, 836–886 (2021)
Sun, Y., Liu, J., Wang, J., et al.: When machine learning meets privacy in 6G: a survey. IEEE Commun. Surv. Tutorials 22(4), 2694–2724 (2020)
Mitrokotsa, A., Komninos, N., Douligeris, C.: Intrusion detection with neural networks and watermarking techniques for MANET. In: IEEE International Conference on Pervasive Services. IEEE, pp. 118–127 (2007)
Shafiq, M., Tian, Z., Bashir, A.K., et al.: CorrAUC: a malicious bot-IoT traffic detection method in IoT network using machine-learning techniques. IEEE Internet Things J. 8(5), 3242–3254 (2020)
Li, L., Yan, J., Wang, H., et al.: Anomaly detection of time series with smoothness-inducing sequential variational auto-encoder. IEEE Trans. Neural Networks Learn. Syst. 32(3), 1177–1191 (2020)
Wang, X., Han, Y., Leung, V.C.M., et al.: Convergence of edge computing and deep learning: a comprehensive survey. IEEE Commun. Surveys Tutorials 22(2), 869–904 (2020)
Sakurada, M., Yairi, T.: Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, pp. 4–11 (2014)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial nets. Advances in Neural Information Processing Systems 27, Curran Associates, Inc. (2014)
Alsharif, M.H., Kelechi, A.H., Albreem, M.A., et al.: Sixth generation (6G) wireless networks: vision, research activities, challenges and potential solutions. Symmetry 12(4), 676 (2020)
Ferrag, M.A., Maglaras, L., Derhab, A.: Authentication and authorization for mobile IoT devices using biofeatures: recent advances and future trends. Secur. Commun. Networks 2019, 1–20 (2019)
Majid, M., et al.: Applications of wireless sensor networks and internet of things frameworks in the industry revolution 4.0: a systematic literature review. Sensors 22(6), 2087 (2022)
Tang, F., Kawamoto, Y., Kato, N., et al.: Future intelligent and secure vehicular network toward 6G: machine-learning approaches. Proc. IEEE 108(2), 292–307 (2019)
Zhang, Z., Cao, Y., Cui, Z., et al.: A many-objective optimization based intelligent intrusion detection algorithm for enhancing security of vehicular networks in 6G. IEEE Trans. Veh. Technol. 70(6), 5234–5243 (2021)
Ahmad, I., Shahabuddin, S., Kumar, T., et al.: Security for 5G and beyond. IEEE Commun. Surveys Tutorials 21(4), 3682–3722 (2019)
S.A., et al.: 6G white paper on machine learning in wireless communication networks. https://arxiv.org/pdf/2004.13875.pdf. Accessed 10 Aug 2021
Anthi, E., Williams, L., Słowińska, M., et al.: A supervised intrusion detection system for smart home IoT devices. IEEE Internet Things J. 6(5), 9042–9053 (2019)
Pu, C.: Sybil attack in RPL-based internet of things: analysis and defenses. IEEE Internet Things J. 7(6), 4937–4949 (2020)
Yang, H., Liang, S., Ni, J., et al.: Secure and efficient k NN classification for industrial Internet of Things. IEEE Internet Things J. 7(11), 10945–10954 (2020)
Ravi, N., Shalinie, S.M.: Semisupervised-learning-based security to detect and mitigate intrusions in IoT network. IEEE Internet Things J. 7(11), 11041–11052 (2020)
Ravi, N., Shalinie, S.M.: Learning-driven detection and mitigation of DDoS attack in IoT via SDN-cloud architecture. IEEE Internet Things J. 7(4), 3559–3570 (2020)
Wang, J., Jiang, C., Zhang, H., et al.: Thirty years of machine learning: the road to Pareto-optimal wireless networks. IEEE Commun. Surveys Tutorials 22(3), 1472–1514 (2020)
Hussain, F., Hussain, R., Hassan, S.A., et al.: Machine learning in IoT security: current solutions and future challenges. IEEE Commun. Surveys Tutorials 22(3), 1686–1721 (2020)
Gao, J., Gan, L., Buschendorf, F., et al.: Omni SCADA intrusion detection using deep learning algorithms. IEEE Internet Things J. 8(2), 951–961 (2020)
Gamage, S., Samarabandu, J.: Deep learning methods in network intrusion detection: a survey and an objective comparison. J. Netw. Comput. Appl. 169, 102767 (2020)
Wu, J., Zhao, Z., Sun, C., et al.: Fault-attention generative probabilistic adversarial autoencoder for machine anomaly detection. IEEE Trans. Industr. Inf. 16(12), 7479–7488 (2020)
Grill, J.B., Strub, F., Altché, F., et al.: Bootstrap your own latent-a new approach to self-supervised learning. Adv. Neural Inf. Process. Syst. 33, 21271–21284 (2020)
CSE-CIC-IDS2018 Dataset, CIC, Fredericton, NB, Canada (2018). https://www.unb.ca/cic/datasets/ids-2018.html. Accessed 4 Oct 2020
Acknowledgment
This work was supported by Heilongjiang Province Natural Science Foundation under Grant LH2022F034.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Li, Y., Sun, Y., He, D., Xi, L. (2023). 6G Network Traffic Intrusion Detection Using Multiresolution Auto-encoder and Feature Matching Discriminator. In: Li, A., Shi, Y., Xi, L. (eds) 6GN for Future Wireless Networks. 6GN 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 504. Springer, Cham. https://doi.org/10.1007/978-3-031-36011-4_18
Download citation
DOI: https://doi.org/10.1007/978-3-031-36011-4_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36010-7
Online ISBN: 978-3-031-36011-4
eBook Packages: Computer ScienceComputer Science (R0)