The host load prediction problem in cloud computing has also been received much attention. To solve this problem, we have to use the historical load data to predict the future load level. Accurate prediction methods a...The host load prediction problem in cloud computing has also been received much attention. To solve this problem, we have to use the historical load data to predict the future load level. Accurate prediction methods are useful for host load balance and virtual machine migration. Although cloud is likely to grids at some extent, the length of tasks are much shorter and host loads change more frequently with higher noise. The above characteristics introduce challenges for host load prediction. In this paper, based on the proposed exponentially segmented pattern and the corresponding transformation, prediction problem is transformed into the traditional classification problem, This classification problem can be solved based on the traditional methods, and features are given for training the classification model. For achieving accurate prediction, a new feature periodical coefficient is introduced and some existed classification methods are implemented. Experiments on the real world dataset invalidate the efficiency of the new proposed feature, which is in the most effective combinations of features, it increases successful rate （SR） 1.33%-2.82% and decreases the mean square error （MSE） 1.37%-2.91%. And the results also show that support vector machine （SVM） method can achieve nearly the same performance as the Bayes methods and their performance is about 50% higher in successful rate and 17% better in the mean square error compared to the existed methods.展开更多
通过分析分布式拒绝服务攻击(Distributed Denial Of Service,DDOS)的特点,提出一种基于主机负载-并发连接时间序列预测的DDOS攻击检测方法。该方法改进了传统的异常检测方法,对并发连接序列进行预测,以预测值作为对未来时段内主机负载...通过分析分布式拒绝服务攻击(Distributed Denial Of Service,DDOS)的特点,提出一种基于主机负载-并发连接时间序列预测的DDOS攻击检测方法。该方法改进了传统的异常检测方法,对并发连接序列进行预测,以预测值作为对未来时段内主机负载正常状态的估计,增强了正常行为描述的时效性,提高了攻击检测率,并具有低延时特性。该方法涉及两项关键技术:一是预测技术,二是异常判断方法。为提高预测精度,首次将小波分析引入主机负载预测,建立了小波-神经网络预测模型;为提高异常判断准确性,采用了“滑动窗口”方式。实验表明,基于负载预测的DDOS攻击检测优于传统的异常检测方法。展开更多
基金supported by the National Key project of Scientific and Technical Supporting Programs of China (2013BAH10F01, 2013BAH07F02, 2014BAH26F02)The Research Fund for the Doctoral Program of Higher Education (20110005120007)+1 种基金Beijing Higher Education Young Elite Teacher Project (YETP0445)The Co-construction Program with Beijing Municipal Commission of Education
文摘The host load prediction problem in cloud computing has also been received much attention. To solve this problem, we have to use the historical load data to predict the future load level. Accurate prediction methods are useful for host load balance and virtual machine migration. Although cloud is likely to grids at some extent, the length of tasks are much shorter and host loads change more frequently with higher noise. The above characteristics introduce challenges for host load prediction. In this paper, based on the proposed exponentially segmented pattern and the corresponding transformation, prediction problem is transformed into the traditional classification problem, This classification problem can be solved based on the traditional methods, and features are given for training the classification model. For achieving accurate prediction, a new feature periodical coefficient is introduced and some existed classification methods are implemented. Experiments on the real world dataset invalidate the efficiency of the new proposed feature, which is in the most effective combinations of features, it increases successful rate （SR） 1.33%-2.82% and decreases the mean square error （MSE） 1.37%-2.91%. And the results also show that support vector machine （SVM） method can achieve nearly the same performance as the Bayes methods and their performance is about 50% higher in successful rate and 17% better in the mean square error compared to the existed methods.
文摘通过分析分布式拒绝服务攻击(Distributed Denial Of Service,DDOS)的特点,提出一种基于主机负载-并发连接时间序列预测的DDOS攻击检测方法。该方法改进了传统的异常检测方法,对并发连接序列进行预测,以预测值作为对未来时段内主机负载正常状态的估计,增强了正常行为描述的时效性,提高了攻击检测率,并具有低延时特性。该方法涉及两项关键技术:一是预测技术,二是异常判断方法。为提高预测精度,首次将小波分析引入主机负载预测,建立了小波-神经网络预测模型;为提高异常判断准确性,采用了“滑动窗口”方式。实验表明,基于负载预测的DDOS攻击检测优于传统的异常检测方法。
