A distributed denial of service attack sources detection technology for cloud computing
Wenjun Yang, Dan Wei · 2017
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.