Abstract
Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM) model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED) algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform.
Highlights
As cloud computing continues to develop, cloud platform based on virtualization technology is becoming increasingly popular in the fields of medicine, biology, geology and scientific computing and so on
Weighted Euclidean Distance (WED) method and a heuristic-based initial method are incorporated into the Self-organizing Map (SOM) algorithm to reduce computational overhead, shorten the training time and self-adaptation in dynamic environment
The different detection models based on KDD Cup dataset and real dataset are trained through experiments 2 and 3 to evaluate the performance
Summary
As cloud computing continues to develop, cloud platform based on virtualization technology is becoming increasingly popular in the fields of medicine, biology, geology and scientific computing and so on. The scale of virtual machines in cloud platform is continuously growing, and the applications deployed on virtual machines are more and more complex. Competition for resources in the cloud platform, resource sharing, virtual machine overload are prone to cause abnormalities which will make a part of the virtual machines downtime and will affect the reliability and availability of the entire cloud platform seriously. It is highly desirable to provide an effective anomaly detection algorithm for virtual machines in cloud platform [1,2,3,4].
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