An Anomaly Intrusion Detection Method Based on Improved K-Means of Cloud Computing
Xinlong Zhao, Weishi Zhang · 2016
With the widely use of cloud computing, security issues become increasingly important. In the scene of cloud computing, traditional intrusion detection methods are not practical. In this paper, a new intrusion detection method based on improved K-means is proposed, which is designed to fit the characteristics and security requirements of cloud computing. The method provides a clustering algorithm and a distributed intrusion detection method based on it. The new method can find out known attack as well as anomaly attack in the environment of cloud computing. The result of simulate test proves that the new method can decrease the false positive and false negative rate, and accelerate the speed of intrusion detection.