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Joint DDoS detection system based on software-defined networking |
| SONG Yubo, YANG Huiwen, WU Wei, HU Aiqun, GAO Shang |
| School of Information Science and Engineering, Southeast University, Nanjing 211189, China |
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Abstract Distributed denial-of-service (DDoS) attacks, which are becoming increasingly serious, have become one of the biggest threats to network security. Traditional defense mechanisms such as instruction detection, traffic filtering and multiple authentication are limited to static networks, which leads to obvious drawbacks. Software-defined networking (SDN) is a typical dynamic network that provides defenses against DDoS. The existing SDN-based DDoS protection solutions are still in development with many problems that need improvement. A DDoS detection scheme combined with trigger detection and in-depth detection is given here to shorten the detection period with low system overhead. A low-overhead, coarse-grained trigger detection algorithm is integrated with a precise, fine-grained, in-depth detection algorithm to reduce system complexity while ensuring high detection accuracy. An SDN DDoS detection system has been implemented on the Mininet platform to test and evaluate the system. The test show that the detection system has low system overhead, high detection accuracy, and strong practical value.
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| Keywords
distributed denial-of-service attack
software-defined networking
anomaly detection
ensemble learning
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Issue Date: 16 January 2019
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2019,
Vol. 59
