基于多特征融合的云平台异常检测方法

doi:10.16451/j.cnki.issn1003-6059.201811005

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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.

Key words： Multi-feature Fusion Cloud Computing Platform Anomaly Detection Extreme Learning Machine

Received: 24 May 2018

ZTFLH:

TP 391.41

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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Cite this article:

ZHANG Jing,REN Yonggong. Anomaly Detection of Cloud Computing Platform Based on Multi-features Fusion[J]. , 2018, 31(11): 1008-1017.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201811005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I11/1008

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