Assistant Detection of Skewed Data Streams Classification in Cloud Security

摘要：

Data stream in the cloud is characterized by imbal anced distribution and concept drift. To solve the problem of classification of skewed and concept drift data stream in cloud security, we present an oneclass classifier dynamic ensemble method which aims at separating virus data, reducing the amount of data analyzed in clouds, improving the efficiency of intrusion detection in cloud security and assisting detection of virus. The proposed method is based on using K-means algorithm to adjust data distribution, makes use of interval estimation combined with AUC value to check concept drift and updates classifiers and dynamically allocates weights. Experimental results illustrate that the proposed method can achieve good classification performance on synthetic dataset and effectively separate most of the virus samples on KDDCUP99 dataset.

关键词： Cloud security imbalanced data stream concept drift classifier ensemble dynamic classifier ensemble

作者: Qun Song Jun Zhang Qian Chi

作者单位: School of Automation Northwest Polytechnical University Xian, P.R.China School of Automation Northwest Polytechnical University Xian,P.R.China College of Mechanical and Electronic Engineering Northwest A&F University Xian, P.R.China

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

会议地点: 厦门

会议语种:英文

页码: 60-64

在线出版日期: 2010-10-29（万方平台首次上网日期，不代表论文的发表时间）