Abstract
Nowadays, the Internet of Things (IoT) has developed rapidly and changed people’s life into a more convenient style. However, a huge number of vulnerable IoT devices are exploited to constitute botnet by many attackers, which forms a serious problem for network security. To solve it, we propose a novel detection and mitigation mechanism. In our method, we use Software Defined Networking (SDN), a promising network architecture, for dropping malicious traffic in propagation path to avoid avalanche effect on the victim server in the traditional network. For the existing works, a lot of time and resources are wasted in using the controller of SDN to detect attacks. Unlike them, we take the features of IoT traffic into consideration and utilize the edge computing to provide local services by putting detection and mitigation method into the OpenFlow (OF) switches of IoT. This achieves a distributed anomaly detection to detect and respond IoT-based DDoS attacks in real time, and avoids the overload of the controller. Machine learning is used in the OF switches with around 99% precision. Experimental results demonstrate that our method is capable to mitigate IoT-based DDoS attacks in a short time.
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Acknowledgements.
This work was supported in part by the Natural Science Foundation of China under Grants 61672092, in part by the Fundamental Research Funds for the Central Universities of China under Grants 2018JBZ103 and Major Scientific, and in part by the Technological Innovation Projects of Shandong Province, China (No. 2019JZZY020128).
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Yang, Y., Wang, J., Zhai, B., Liu, J. (2019). IoT-Based DDoS Attack Detection and Mitigation Using the Edge of SDN. In: Vaidya, J., Zhang, X., Li, J. (eds) Cyberspace Safety and Security. CSS 2019. Lecture Notes in Computer Science(), vol 11983. Springer, Cham. https://doi.org/10.1007/978-3-030-37352-8_1
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DOI: https://doi.org/10.1007/978-3-030-37352-8_1
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