Abstract
Distributed Denial-of-Service (DDoS) attacks disrupts the availability of essential services, which are one of the most harmful threats in today’s Internet. Many DDoS detection algorithms based on machine learning technology have emerged in recent years, for example, the LUCID algorithm, GNN model. But considering that DDoS attacks are based on both time and space, these algorithms only considered time and ignored space. Besides, by piece network traffic data is often difficult to obtain. The model we used in this paper is based on the Spatio-Temporal Graph Convolutional Network (STGCN) proposed by Yu B. et al. And on this basis, the model is improved for the unique characteristics of DDoS attacks. By considering the time-dependence of the network topology and network traffic, this model has a good recognition rate on the online DDoS data set, and can achieve detection of DDoS attacks under the premise of using only two-way traffic information.
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Acknowledgments
This study is supported by supported by The National Key Research and Development Program of China under grant 2017YFB0802704 and 2017YFB0802202.
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Xie, Q., Huang, Z., Guo, J., Qiu, W. (2020). Spatio-Temporal Graph Convolutional Networks for DDoS Attack Detecting. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12486. Springer, Cham. https://doi.org/10.1007/978-3-030-62223-7_13
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DOI: https://doi.org/10.1007/978-3-030-62223-7_13
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