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ANOMALY DETECTION METHOD FOR INTERNAL VIRTUAL MACHINE OF CLOUD SYSTEM

Abstract(summary):

An anomaly detection method for an internal virtual machine of a cloud system. State information about a normal virtual machine in a cloud system is collected to train a Hidden Semi-Markov Model (HsMM), and a corresponding algorithm is designed for detecting and computing probabilities and mahalanobis distances of resource dynamic change behaviour when various virtual machines in the cloud system are on line. If the mahalanobis distance in an on-line detection result of a certain virtual machine is greater than a pre-set threshold value, this indicates that an activity of the virtual machine is abnormal, so an anomaly detection and processing system in the cloud system is started for performing anomaly detection and processing on the virtual machine. If it is detected that an anomaly rate of a certain virtual machine is less than the maximum threshold value of the anomaly detection and processing, after an anomaly is eliminated, a warning prompt is sent to a cloud tenant of the virtual machine; and otherwise, an alarm is given to the cloud tenant of the virtual machine, and the virtual machine is shut down. According to the method, abnormal behaviour of an internal virtual machine of a cloud system can be detected in real time, and fewer system resources are occupied, thereby fully ensuring the high availability and security of an internal virtual machine of a cloud system.


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