Abstract A multi-feature fusion model based on distance constraint and solution space optimization is proposed to utilize the information of different sub-systems in the cloud computing platform and enhance the performance of anomaly detection. The minimum of the errors-sum from all of the single sub-system is obtained to achieve the optimal solution and the fusion of multi-features by iterating, and the high power coefficient is introduced to avoid the degenerating. Moreover, the proposed method is developed as an incremental learning method to ensure the real-time performance. The proposed method reduces the redundant information between high-dimension features and meanwhile mines the latent knowledge of different sub-systems in cloud platform. Thus, the performance in anomaly detection is improved. The private cloud platform based on OpenStack is constructed, and the real-time collection of data is implemented to verify the effectiveness of the proposed method. Compared with the state-of-the-art methods of anomaly detection in cloud platform, the proposed method achieves better accuracy.
Fund:Supported by National Natural Science Foundation of China(No.61772252,61702242), Doctoral Scientific Research Foundation of Liaoning Province(No.20170520207)
Corresponding Authors:
REN Yonggong, Ph.D., professor. His research interests include data mining.
About author:: ZHANG Jing, Ph.D., lecturer. Her research interests include machine learning and pattern classification.
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2018, Vol. 31 
