一种基于梯度提升的云安全机器学习算法

A Gradient Boosting Machine Learning Algorithm for CloudSecurity

近年来,云计算技术飞速发展,许多企业和机构将自己的业务迁移到云上,这样不仅降低费用,还能提高效率。但随之而来的是云服务提供者和用户被大量的恶意软件攻击。许多机器学习算法通过对云平台上可能发生的行为进行预测,来保护云系统不受攻击,取得了不错的效果。但当所学习的数据集较大和稀疏时,这些机器学习算法效果不是很好。本文采用了一种梯度提升的决策树算法,能对云计算系统上的恶意软件攻击进行更准确的预测。实验验证了本方法的有效性。

In recent years,with the rapid development of cloud computing technology,many enterprises andinstitutions transfer their business to the cloud,which not only reduces costs,but also provides efficiency.But then it is easier for cloud service providers being attacked by a large number of malware.Many machinelearning algorithms are used to protect the cloud system from attack by predicting the possible behavior onthe cloud platform,and achieved good performance.However,when the data set is large and sparse,the effectof these machine learning algorithms is not good.In this paper,a gradient boosting decision tree algorithmis adopted,which can more accurately predict the malware attacks on cloud computing system.Experimentresults show the effectiveness of the proposed method.