云计算环境下异常信息优化检测仿真

摘要

对云计算环境下异常信息的优化检测,能够有效解决云计算环境中信息存储以及传输过程的安全问题.对异常信息的检测,需要定义异常信息行为的属性值,计算出异常信息行为分类器的输出最大目标概率值,完成异常信息的优化检测.传统方法先给出异常信息的调用流图特征,从而给出异常信息的“行为”特性,但忽略了计算出异常信息的最大目标概率值,导致检测精度偏低.提出基于朴素贝叶斯的云计算环境下异常信息优化检测方法.上述方法利用字符矩方法提取出异常信息的样本特征,得到未知异常信息中的每个分块空间的关系特征向量,给出异常信息行为模式在朴素贝叶斯中的表述方程,定义异常信息行为的属性值,计算出异常信息的输出最大目标概率值,并完成对云计算环境下异常信息优化检测.实验结果表明,所提方法检测精度较高,为保障云计算环境的安全稳定运行奠定了基础.%This research focuses on optimization detection method for abnormalinformation in cloud computing environment.The research used character moment method to extract sample feature of abnormal information and obtained vector of relationship feature of each block space in unknown abnormal information.Expression function of behavior pattern of abnormal information in naive Bayes was provided and attribute value of the abnormal information behavior was defined.The research also worked out maximum target probability value of input of abnormal information.Experimental results show that the method has higher detection precision.It lays a foundation for ensuring safe and stable operation of cloud computing environment.