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A distributed denial of service attack sources detection technology for cloud computing

机译:一种用于云计算的分布式拒绝服务攻击源检测技术

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With the development of cloud computing technology, the usage cost and threshold of cloud environment is gradually reduced. More and more sources of distributed denial of service (DDoS) attacks appear in the cloud environment, which poses a serious threat to the security of the cloud space, but also consumes a large amount of cloud resources. The difficulty of DDoS attack detection in cloud environment is how to detect small traffic attacks. Based on this, a DDoS attack detection method based on traffic entropy and Naive Bayes proposed, which is detected by calculating the traffic entropy and combining with the Naive Bayes algorithm. It can identify attack traffic from potential traffic, and locate the attack source virtual machine according to characteristics of cloud environment. The experimental result reveals that the method proposed yields the best performance opposed to SVM and K-nearest.
机译:随着云计算技术的发展,云环境的使用成本和门槛逐渐降低。云环境中出现了越来越多的分布式拒绝服务(DDoS)攻击源,这对云空间的安全性构成了严重威胁,但同时也消耗了大量的云资源。云环境下DDoS攻击检测的难点在于如何检测小流量攻击。在此基础上,提出了一种基于流量熵和朴素贝叶斯的DDoS攻击检测方法,通过计算流量熵并结合朴素贝叶斯算法进行检测。它可以从潜在流量中识别攻击流量,并根据云环境的特征来定位攻击源虚拟机。实验结果表明,所提出的方法具有与SVM和K近邻相反的最佳性能。

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