Ddos attack detection based on one-class svm in sdn
文献类型：会议论文
| 作者 | Zhao JM(赵剑明)1,2,3,4 ; Zeng P(曾鹏)1,2,3,4; Shang WL(尚文利)1,2,3,4; Tong GY(佟国毓)1,2,3,4
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| 出版日期 | 2020 |
| 会议日期 | July 17-20, 2020 |
| 会议地点 | Hohhot, China |
| 关键词 | DDoS attack detection One-class SVM SDN Feature vector |
| 页码 | 189-200 |
| 英文摘要 | Software Defined Networking (SDN) is a new type of network architecture, which provides an important way to implement automated network deployment and flexible management. However, security problems in SDN are also inevitable. DDoS attack belongs to one of the most serious attack types, which is fairly common for today’s Internet. In SDN security fields, DDoS attack detection research has been received more and more attention. In this paper, a DDoS attack detection method based on one-class SVM in SDN is proposed, which provides a better detection accuracy. Furthermore, two new feature vectors, including middle value of flow table item’s duration and protocol data traffic percentage, are extracted to integrate into the item of 11 feature vectors. Additionally, basing on selection and construction method of the 11 feature vectors, a DDoS attack behavior model is established by using one-class SVM algorithm, and the self-adaptation genetic algorithm is designed to optimize the corresponding parameters of the Gaussian kernel of one-class SVM. The experimental results in SDN show that, the proposed new feature vectors are shown to more better detection accuracy, and the proposed method is more feasible by comparing with the BP neural network and RBF neural network algorithms under the same 11 features vectors. |
| 产权排序 | 1 |
| 会议录 | Artificial Intelligence and Security - 6th International Conference, ICAIS 2020, Proceedings
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| 会议录出版者 | Springer Science and Business Media Deutschland GmbH |
| 会议录出版地 | Berlin |
| 语种 | 英语 |
| ISSN号 | 1865-0929 |
| ISBN号 | 978-981-15-8100-7 |
| 源URL | [http://ir.sia.cn/handle/173321/27691]  |
| 专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
| 通讯作者 | Zeng P(曾鹏) |
| 作者单位 | 1.University of Chinese Academy of Sciences, Beijing 100049, China 2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China 3.Key Laboratory of Networked Control System, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 4.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China |
| 推荐引用方式 GB/T 7714 | Zhao JM,Zeng P,Shang WL,et al. Ddos attack detection based on one-class svm in sdn[C]. 见:. Hohhot, China. July 17-20, 2020. |
入库方式： OAI收割
来源：沈阳自动化研究所
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