An online anomaly detection method for stream data using isolation principle and statistic histogram
作者机构：Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronics Engineering and Automation Shanghai University Shanghai 200072P.R.China College of MathematicsPhysics and Information Engineering Zhejiang Normal University JinhuaZhejiang 321004P.R.China
出 版 物：《International Journal of Modeling, Simulation, and Scientific Computing》 (建模、仿真和科学计算国际期刊（英文）)
年 卷 期：2015年第6卷第2期
页 面：85-106页
核心收录：
学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学]
基 金：This work is supported by the National Key Scientific Instrument and Equipment Development Project(2012YQ15008703) The Open Project of Top Key Discipline of Computer Software and Theory in Zhejiang Provincial(ZC323014100) National Science Foundation of China(61104089,61473182) Science and Technology Commission of Shanghai Municipality(11JC1404000,14JC1402200) Shanghai RisingStar Program(13QA1401600)
主 题：Online anomaly detection stream data isolation principle ensemble learning statistic histogram
摘 要：Online anomaly detection for stream data has been explored recently,where the detector is supposed to be able to perform an accurate and timely judgment for the upcoming ***,due to the inherent complex characteristics of stream data,such as quick generation,tremendous volume and dynamic evolution distribution,how to develop an effective online anomaly detection method is a *** main objective of this paper is to propose an adaptive online anomaly detection method for stream *** is achieved by combining isolation principle with online ensemble learning,which is then optimized by statistic *** main algorithms are developed,i.e.,online detector building algorithm,anomaly detecting algorithm and adaptive detector updating *** evaluate our proposed method,four massive datasets from the UCI machine learning repository recorded from real events were *** simulations based on these datasets show that our method is effective and robust against different scenarios.
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