Abstract
Edge computing extends the traditional cloud computing architecture by using the computing and storage resources on the edge of the network, making people’s work and life more convenient. However, these devices at the edge of the network are widely distributed and the environment is relatively complex. Attackers use these vulnerable IoT devices to build botnets to initiate distributed denial of service attacks, posing a serious threat to the normal use of such networks. In response to this problem, we propose an anomaly detection framework based on software-defined networking (SDN). The edge controller in the SDN network is used to obtain the flow information and extract the features of the flow. The XGBoost algorithm optimized by genetic algorithm (GA-XGBoost) we proposed is used to classify and detect the flow. Experimental results show that compared with other algorithms of the same type, our proposed algorithm has a lower false alarm rate and higher accuracy.
This work was supported by the National Natural Science Foundation of China (61572340), Advance Research Fund (No. 61403120402), Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD); Collaborative Innovation Center of Novel Software Technology and Industrialization of Universities in Jiangsu Province.
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Ren, G., Zhang, Y., Zhang, S., Long, H. (2022). Edge DDoS Attack Detection Method Based on Software Defined Networks. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13155. Springer, Cham. https://doi.org/10.1007/978-3-030-95384-3_37
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