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基于信息密度贝叶斯算法的云平台入侵检测

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为更好实现对云平台入侵检测数据的分类处理,提升检测精度和性能,提出了一种基于信息密度的贝叶斯算法.构造完整的数据特征概率集合,通过引入信息熵来表示信息的不确定度,并定义了信息密度以描述信息不确定度分布状态.对算法的收敛性和时间复杂度进行了分析,并进行仿真实验,与已有技术相比,方法可有效减少数据信息损失和描述数据特征与数据类型的概率关系,能够准确将云平台入侵检测数据分类,具有较高的检测率和较低的误报率.
Information Density based Bayes Algorithm for Cloud Platform Intrusion Detection
For getting better data classification results of cloud platform intrusion detection,and improving the detection accuracy and performance,a Bayes algorithm based on information density was proposed.The complete probability of data characteristics was constructed,and the uncertainty of information was represented by information entropy.The information density was defined to describe the distribution of information uncertainty.The improved algorithm was introduced,and the convergence and time complexity were analyzed.The simulation experiment results show that the method can effectively reduce the data loss and exposethe relationship between data characteristics and data type,which can further classify the detection data of cloud platform accurately with high detection rate and low false positive rate.

cloud platformintrusion detectiondata characteristicsinformation densityBayes algorithm

杜晔、张田甜、黎妹红

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北京交通大学计算机与信息技术学院,北京100044

云平台 入侵检测 数据特征 信息密度 贝叶斯算法

中央高校基本科研业务费北京高校青年英才计划基金

2014JBM030YETP0548

2018

系统仿真学报
北京仿真中心 中国系统仿真学会

系统仿真学报

CSTPCDCSCD北大核心
影响因子:0.551
ISSN:1004-731X
年,卷(期):2018.30(2)
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