期刊
IEEE ACCESS
卷 8, 期 -, 页码 5039-5048出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2963077
关键词
DDoS attack; traffic behavior; software defined networking; gain value
资金
- Key Scientific and Technological Research Projects in Henan Province [192102210125]
- Study Abroad Activities of Science and Technology Project of Henan Province
The Distributed Denial of Service (DDoS) attack has seriously impaired network availability for decades and still there is no effective defense mechanism against it. However, the emerging Software Defined Networking (SDN) provides a newway to reconsider the defense against DDoS attacks. In this paper, we propose two methods to detect the DDoS attack in SDN. One method adopts the degree of DDoS attack to identify the DDoS attack. The other method uses the improved K-Nearest Neighbors (KNN) algorithm based on Machine Learning (ML) to discover the DDoS attack. The results of the theoretical analysis and the experimental results on datasets show that our proposed methods can better detect the DDoS attack compared with other methods.
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