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DDoS Detection based on Wavelet Kernel Support Vector Machine

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分类：TP393.08[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
作者机构：[1]College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
相关基金：Acknowledgements This work is supported by National Natural Science Foundation of China （60573141, 60773041）, the Hi-Tech Research and Development Program of China （2006AA01ZA39）, Natural Science Foundation of Jiangsu Province （BK2005146）, High Technology Research Program of Jiangsu Province （BG2005037, BG2006001）, Key Laboratory of Information Technology Processing of Jiangsu Province （kjs0606）, High Technology Research Program of Nanjing City （2006RZ105） , State Key Laboratory of Modem Communication （9140C 1101010603）, Jiangsu Provincial Research Scheme of Natural Science for Higher Education Institutions （07KJB520083）.
相关项目：应用移动代理的网格安全关键技术的研究

关键词：网络安全, 网络攻击, 小波核函数, 小波支持向量机, DDOS, wavelet kernel function, wavelet supporting vector machine, DDoS detection

中文摘要：

为了提高察觉精确性并且推出分布式的拒绝服务(DDoS ) 的假积极的率，攻击察觉，一个新机器学习方法被建议。与支持向量机器(SVM ) 和小浪核功能理论的分析，一个容许有的支持向量核，是在这篇文章构造的一个小浪核，与 SVM 实现小浪技术的联合。然后，小浪支持向量机器(WSVM ) 被用于 DDoS 攻击察觉并且作为一个分类工具测试小浪内核函数的有效性。模拟实验证明在一样的条件下面， WSVM 的预兆的能力被改进，计算负担被减轻。WSVM 的察觉精确性比在大约 4% 的传统的 SVM 高，当时它的假积极比传统的 SVM 低。为 DDoS 察觉，因此， WSVM 显示出更好的察觉性能并且对变化网络环境更适应。

英文摘要：

To enhance the detection accuracy and deduce false positive rate of distributed denial of service （DDoS） attack detection, a new machine learning method was proposed. With the analysis of support vector machine （SVM） and the wavelet kernel function theory, an admissive support vector kernel, which is a wavelet kernel constructed in this article, implements the combination of the wavelet technique with SVM. Then, wavelet support vector machine （WSVM） is applied to DDoS attack detections and as a classifying means to test the validity of the wavelet kernel function. Simulation experiments show that under the same conditions, the predictive ability of WSVM is improved and the computation burden is alleviated. The detection accuracy of WSVM is higher than the traditional SVM by about 4%, while its false positive is lower than the traditional SVM. Thus, for DDoS detections, WSVM shows better detection performance and is more adaptive to the changing network environment.