摘要
云环境中服务运行数据是服务运行状态的反映,如果服务运行数据出现异常将会影响相关软件的运行和用户的使用。传统的软件异常检测方法通常忽略软件运行数据各维度属性提供的信息量及软件运行时的上下文环境,从而影响异常检测的准确率。因此,提出一种加权LOF结合上下文判断的云环境中服务运行数据异常检测方法,首先使用信息熵法给服务运行数据的各维度属性赋权,使用改进的加权LOF算法对服务运行数据进行初次异常判断;然后综合考虑服务运行时的上下文信息,对服务运行数据进行二次异常判断后得到相应结果。实验表明,此方法能够有效检测出云环境中的服务运行数据异常。
Service running data reflects the service running state in the cloud environment.If there are service running data anomalies in the cloud environment,the operation of related software and the use of users will be affected.Traditional software anomaly detection methods usually neglect the information quantity provided by different dimension attributes of running data and the context environment of software running.Thus,the anomaly detection is inaccurate.Therefore,A service running data anomaly detection method based on weighted LOF and context judgment in cloud environment is proposed.Firstly,the dimension attributes of running data are weighted by the information entropy method,and the running data are judged by weighted LOF algorithm for the first anomaly detection.Secondly,the context information of service at runtime is considered comprehensively.The correspon-ding results are obtained after the second anomaly detection.Experiments show that this method can effectively detect service running data anomalies in the cloud environment.
作者
仇开
姜瑛
QIU Kai;JIANG Ying(Computer Technology Application Key Laboratory of Yunnan Province,Kunming 650500;Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China)
出处
《计算机工程与科学》
CSCD
北大核心
2020年第6期951-958,共8页
Computer Engineering & Science
基金
国家自然科学基金(61462949,61063006,60703116)
云南省应用基础研究计划重点项目基金(2017FA033)。
