{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T06:44:15Z","timestamp":1759041855538,"version":"3.37.3"},"reference-count":34,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"21","license":[{"start":{"date-parts":[[2017,11,1]],"date-time":"2017-11-01T00:00:00Z","timestamp":1509494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2017,11,1]],"date-time":"2017-11-01T00:00:00Z","timestamp":1509494400000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2017,11,1]],"date-time":"2017-11-01T00:00:00Z","timestamp":1509494400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2017,11,1]],"date-time":"2017-11-01T00:00:00Z","timestamp":1509494400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CCF-10-26565","CCF-10-26565","CCF-16-17789","CMMI-1435778","ECCS-1343210","DGE-1523154","IIS-1502172"],"award-info":[{"award-number":["CCF-10-26565","CCF-10-26565","CCF-16-17789","CMMI-1435778","ECCS-1343210","DGE-1523154","IIS-1502172"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Signal Process."],"published-print":{"date-parts":[[2017,11,1]]},"DOI":"10.1109\/tsp.2017.2733472","type":"journal-article","created":{"date-parts":[[2017,7,31]],"date-time":"2017-07-31T18:07:10Z","timestamp":1501524430000},"page":"5785-5797","source":"Crossref","is-referenced-by-count":26,"title":["Nonparametric Detection of Anomalous Data Streams"],"prefix":"10.1109","volume":"65","author":[{"given":"Shaofeng","family":"Zou","sequence":"first","affiliation":[]},{"given":"Yingbin","family":"Liang","sequence":"additional","affiliation":[]},{"given":"H. Vincent","family":"Poor","sequence":"additional","affiliation":[]},{"given":"Xinghua","family":"Shi","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"year":"1987","key":"ref33","article-title":"CPC global summary of day\/month observations, 1979-continuing"},{"article-title":"Adaptivity and computation-statistics tradeoffs for kernel and\n distance based high dimensional two sample testing","year":"2015","author":"ramdas","key":"ref32"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2006.07.009"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2017.8006676"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.5194\/hessd-4-439-2007"},{"key":"ref10","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"gretton","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-9096-9"},{"key":"ref12","first-page":"1517","article-title":"Hilbert space embeddings and metrics on probability\n measures","volume":"11","author":"sriperumbudur","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176344722"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/89.2.359"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2011.2163380"},{"key":"ref16","first-page":"609","article-title":"Testing\n for homogeneity with kernel fisher discriminant analysis","author":"harchaoui","year":"2008","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-007-9033-z"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2007.02.001"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"},{"key":"ref28","article-title":"Generative models and model criticism via\n optimized maximum mean discrepancy","author":"sutherland","year":"0","journal-title":"Proc Int Conf on Learning Rep"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2885508"},{"key":"ref27","first-page":"1205","article-title":"Optimal kernel choice for large-scale\n two-sample tests","author":"gretton","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2013.2253351"},{"key":"ref6","article-title":"Data-driven approaches for detecting and identifying\n anomalous data streams (to appear)","author":"zou","year":"2017","journal-title":"Biomedical Signal Processing in Big Data"},{"key":"ref29","first-page":"1981","article-title":"Fast two-sample testing\n with analytic representations of probability measures","author":"chwialkowski","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972733.30"},{"key":"ref8","first-page":"449","article-title":"One-class support measure machines for group anomaly dection","author":"muandet","year":"2013","journal-title":"Proc Conf Uncertain Artif Intell"},{"article-title":"System and methods for adaptive model\n generation for detecting intrusion in computer systems","year":"2016","author":"honig","key":"ref7"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2011.2159038"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2014.2317691"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ALLERTON.2014.7028541"},{"key":"ref20","first-page":"585","article-title":"Geometric entropy minimization (GEM) for anomaly detection and\n localization","author":"hero","year":"2006","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref22","first-page":"599","article-title":"Nonparametric divergence estimation with applications to machine learning on distributions","author":"p\u00f3czos","year":"2011","journal-title":"Proc Conf Uncertain Artif Intell"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/18.782114"},{"key":"ref24","article-title":"Nonparametric divergence estimation for learning manifolds of distributions and group anomaly detection","author":"poczos","year":"2011","journal-title":"The Learning Workshop (Snowbird)"},{"key":"ref23","first-page":"1071","article-title":"Group anomaly detection using flexible genre models","author":"xiong","year":"2011","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref26","first-page":"111","article-title":"Injective Hilbert space embeddings of probability\n measures","author":"sriperumbudur","year":"2008","journal-title":"Proc Annu Conf Learn Theory"},{"key":"ref25","first-page":"489","article-title":"Kernel measures of conditional\n dependence","author":"fukumizu","year":"2008","journal-title":"Proc Adv Neural Inf Process Syst"}],"container-title":["IEEE Transactions on Signal Processing"],"original-title":[],"link":[{"URL":"http:\/\/ieeexplore.ieee.org\/ielaam\/78\/8023016\/7997789-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/78\/8023016\/07997789.pdf?arnumber=7997789","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T18:49:02Z","timestamp":1649443742000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/7997789\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,11,1]]},"references-count":34,"journal-issue":{"issue":"21"},"URL":"https:\/\/doi.org\/10.1109\/tsp.2017.2733472","relation":{},"ISSN":["1053-587X","1941-0476"],"issn-type":[{"type":"print","value":"1053-587X"},{"type":"electronic","value":"1941-0476"}],"subject":[],"published":{"date-parts":[[2017,11,1]]}}}