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基于非参数化方法的流数据异常点并行检测

Int. J. Synth. Emot. Pub Date : 2017-07-01 DOI:10.4018/IJSE.2017070102
H. D. Markad, S. Sangve
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引用次数: 4

摘要

Outlierdetectionisusedinvariousapplicationslikedetectionoffraud，networkanalysis，monitoring trafficovernetworks，manufacturingandenvironmentalsoftware。Thedatastreamswhicharegenerated arecontinuousandchangingovertime。Thisisthereasonwhyitbecomesnearlydifficulttodetect theoutliersintheexistingdatawhichishugeandcontinuousinnature。Thestreameddataisreal timeandchangesovertimeandhenceitisimpracticaltostoredatainthedataspaceandanalyze itforabnormalbehavior。Thelimitationsindataspacehasledtotheproblemofrealtimeanalysis ofdataandprocessingit inFCFSbasis。Theresultsregardingtheabnormalbehaviorhavetobe doneveryquicklyandinalimitedtimeframeandonaninfinitesetofdatastreamscomingoverthe网络。Toaddresstheproblemofdetectingoutliersonareal-timebasisisachallengingtaskand hencehastobemonitoredwiththehelpoftheprocessingpowerusedtodesignthegraphicsofany processingunit.Thealgorithmusedinthispaperusesakernelfunctiontoaccomplishthetask。It producestimelyoutcomeonhighspeedmulti-dimensionaldata。Thismethodincreasesthespeed ofoutlierdetectionby20timesandthespeedgoesonincreasingwiththeincreasewiththenumber ofdataattributesandinputdatarate。关键词异常入侵检测，计算统一设备架构(CUDA)，高斯检测方案，图形处理单元(GPU)，离群点检测，并行执行
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Parallel Outlier Detection for Streamed Data Using Non-Parameterized Approach
Outlierdetectionisusedinvariousapplicationslikedetectionoffraud,networkanalysis,monitoring trafficovernetworks,manufacturingandenvironmentalsoftware.Thedatastreamswhicharegenerated arecontinuousandchangingovertime.Thisisthereasonwhyitbecomesnearlydifficulttodetect theoutliersintheexistingdatawhichishugeandcontinuousinnature.Thestreameddataisreal timeandchangesovertimeandhenceitisimpracticaltostoredatainthedataspaceandanalyze itforabnormalbehavior.Thelimitationsindataspacehasledtotheproblemofrealtimeanalysis ofdataandprocessingit inFCFSbasis.Theresultsregardingtheabnormalbehaviorhavetobe doneveryquicklyandinalimitedtimeframeandonaninfinitesetofdatastreamscomingoverthe networks.Toaddresstheproblemofdetectingoutliersonareal-timebasisisachallengingtaskand hencehastobemonitoredwiththehelpoftheprocessingpowerusedtodesignthegraphicsofany processingunit.Thealgorithmusedinthispaperusesakernelfunctiontoaccomplishthetask.It producestimelyoutcomeonhighspeedmulti-dimensionaldata.Thismethodincreasesthespeed ofoutlierdetectionby20timesandthespeedgoesonincreasingwiththeincreasewiththenumber ofdataattributesandinputdatarate. KEywORDS Anomaly Intrusion Detection, Compute Unified Device Architecture (CUDA), Gaussian Detection Scheme, Graphics Processing Unit (GPU), Outlier Detection, Parallel Execution
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Int. J. Synth. Emot.
Int. J. Synth. Emot.
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