{"id":"https://openalex.org/W2963905082","doi":"https://doi.org/10.1109/mlsp.2015.7324326","title":"Detecting clusters of anomalies on low-dimensional feature subsets with application to network traffic flow data","display_name":"Detecting clusters of anomalies on low-dimensional feature subsets with application to network traffic flow data","publication_year":2015,"publication_date":"2015-09-01","ids":{"openalex":"https://openalex.org/W2963905082","doi":"https://doi.org/10.1109/mlsp.2015.7324326","mag":"2963905082"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp.2015.7324326","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2015.7324326","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112457335","display_name":"Zhicong Qiu","orcid":null},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zhicong Qiu","raw_affiliation_strings":["School of EECS, The Pennsylvania State University"],"affiliations":[{"raw_affiliation_string":"School of EECS, The Pennsylvania State University","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101739086","display_name":"David J. Miller","orcid":"https://orcid.org/0000-0001-8848-1643"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David J. Miller","raw_affiliation_strings":["School of EECS, The Pennsylvania State University"],"affiliations":[{"raw_affiliation_string":"School of EECS, The Pennsylvania State University","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063903486","display_name":"George Kesidis","orcid":"https://orcid.org/0000-0001-7947-8127"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"George Kesidis","raw_affiliation_strings":["School of EECS, The Pennsylvania State University"],"affiliations":[{"raw_affiliation_string":"School of EECS, The Pennsylvania State University","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5112457335"],"corresponding_institution_ids":["https://openalex.org/I130769515"],"apc_list":null,"apc_paid":null,"fwci":0.9985,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.81741317,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"14","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6922869682312012},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6768535375595093},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6446671485900879},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6275665760040283},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5820959806442261},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5329136848449707},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5256577730178833},{"id":"https://openalex.org/keywords/independence","display_name":"Independence (probability theory)","score":0.5198776721954346},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.5169119834899902},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4682546555995941},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.45522770285606384},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43242406845092773},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.4282761216163635},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37488704919815063},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1710028052330017},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09189996123313904}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6922869682312012},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6768535375595093},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6446671485900879},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6275665760040283},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5820959806442261},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5329136848449707},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5256577730178833},{"id":"https://openalex.org/C35651441","wikidata":"https://www.wikidata.org/wiki/Q625303","display_name":"Independence (probability theory)","level":2,"score":0.5198776721954346},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.5169119834899902},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4682546555995941},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.45522770285606384},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43242406845092773},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.4282761216163635},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37488704919815063},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1710028052330017},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09189996123313904},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp.2015.7324326","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2015.7324326","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1543388142","https://openalex.org/W1985987493","https://openalex.org/W2019353163","https://openalex.org/W2023035732","https://openalex.org/W2027664152","https://openalex.org/W2087615914","https://openalex.org/W2122192957","https://openalex.org/W2142301198","https://openalex.org/W2146022760","https://openalex.org/W2163166770","https://openalex.org/W2170325549","https://openalex.org/W2406037958","https://openalex.org/W6632547301","https://openalex.org/W6681425521","https://openalex.org/W6685260713","https://openalex.org/W6713761039"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4377864969","https://openalex.org/W2972971679"],"abstract_inverted_index":{"In":[0,95],"a":[1,15,23,27,66,75,91,100],"variety":[2],"of":[3,10,30,35,71,78,109,113,162,170],"applications,":[4],"one":[5],"desires":[6],"to":[7,22,106,124,128,176],"detect":[8,129],"groups":[9,87],"anomalous":[11,86,118],"data":[12,93,158],"samples,":[13],"with":[14],"group":[16,56,77,101],"potentially":[17],"manifesting":[18],"its":[19],"atypicality":[20],"(relative":[21],"reference":[24],"model)":[25],"on":[26,153],"low-dimensional":[28],"subset":[29,70],"the":[31,149,160,168,177],"full":[32],"measured":[33,150],"set":[34],"features.":[36,151],"Samples":[37],"may":[38,46,146],"only":[39],"be":[40,47],"weakly":[41],"atypical":[42,49],"individually,":[43],"whereas":[44],"they":[45],"strongly":[48],"when":[50],"considered":[51],"jointly.":[52],"What":[53],"makes":[54],"this":[55,96],"anomaly":[57,102],"detection":[58,103,127],"problem":[59],"quite":[60],"challenging":[61],"is":[62,65,82],"that":[63,115,145],"it":[64,81],"priori":[67],"unknown":[68,83],"which":[69],"features":[72,114],"jointly":[73,116],"manifests":[74],"particular":[76],"anomalies.":[79],"Moreover,":[80],"how":[84],"many":[85],"are":[88],"present":[89],"in":[90,184],"given":[92],"batch.":[94],"work,":[97],"we":[98],"develop":[99],"(GAD)":[104],"scheme":[105],"identify":[107],"subsets":[108,112],"samples":[110],"and":[111,131,141,166],"specify":[117],"clusters.":[119,134],"We":[120],"apply":[121],"our":[122,138,163],"approach":[123,139],"network":[125,156],"intrusion":[126],"botnet":[130],"peer-to-peer":[132],"flow":[133],"Unlike":[135],"previous":[136,185],"studies,":[137],"captures":[140],"exploits":[142],"statistical":[143],"dependencies":[144],"exist":[147],"between":[148],"Experiments":[152],"real":[154],"world":[155],"traffic":[157],"demonstrate":[159],"advantage":[161],"proposed":[164],"system,":[165],"highlight":[167],"importance":[169],"exploiting":[171],"feature":[172,178],"dependency":[173],"structure,":[174],"compared":[175],"(or":[179],"test)":[180],"independence":[181],"assumption":[182],"made":[183],"studies.":[186]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
