{"id":"https://openalex.org/W3037468556","doi":"https://doi.org/10.1109/wcncw48565.2020.9124843","title":"Anomaly Detection in Mobile Networks","display_name":"Anomaly Detection in Mobile Networks","publication_year":2020,"publication_date":"2020-04-01","ids":{"openalex":"https://openalex.org/W3037468556","doi":"https://doi.org/10.1109/wcncw48565.2020.9124843","mag":"3037468556"},"language":"en","primary_location":{"id":"doi:10.1109/wcncw48565.2020.9124843","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcncw48565.2020.9124843","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","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/A5051372626","display_name":"Anish Nediyanchath","orcid":null},"institutions":[{"id":"https://openalex.org/I4210139030","display_name":"Samsung (India)","ror":"https://ror.org/04cpx2569","country_code":"IN","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210139030"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Anish Nediyanchath","raw_affiliation_strings":["Samsung R&D Institute, Bangalore, India"],"affiliations":[{"raw_affiliation_string":"Samsung R&D Institute, Bangalore, India","institution_ids":["https://openalex.org/I4210139030"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064221720","display_name":"Chirag Singh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chirag Singh","raw_affiliation_strings":["Primary Contributor for MSCRED"],"affiliations":[{"raw_affiliation_string":"Primary Contributor for MSCRED","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016613624","display_name":"Harman Jit Singh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Harman Jit Singh","raw_affiliation_strings":["Contribution as Domain Experts, KPI Generation and Ground Truth formulation"],"affiliations":[{"raw_affiliation_string":"Contribution as Domain Experts, KPI Generation and Ground Truth formulation","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055957791","display_name":"Himanshu Mangla","orcid":null},"institutions":[{"id":"https://openalex.org/I4210139030","display_name":"Samsung (India)","ror":"https://ror.org/04cpx2569","country_code":"IN","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210139030"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Himanshu Mangla","raw_affiliation_strings":["Samsung R&D Institute, Bangalore, India"],"affiliations":[{"raw_affiliation_string":"Samsung R&D Institute, Bangalore, India","institution_ids":["https://openalex.org/I4210139030"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037519320","display_name":"Karan Mangla","orcid":null},"institutions":[{"id":"https://openalex.org/I4210139030","display_name":"Samsung (India)","ror":"https://ror.org/04cpx2569","country_code":"IN","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210139030"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Karan Mangla","raw_affiliation_strings":["Samsung R&D Institute, Bangalore, India"],"affiliations":[{"raw_affiliation_string":"Samsung R&D Institute, Bangalore, India","institution_ids":["https://openalex.org/I4210139030"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033185764","display_name":"Manoj K. Sakhala","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Manoj K. Sakhala","raw_affiliation_strings":["Contribution as Domain Experts, KPI Generation and Ground Truth formulation"],"affiliations":[{"raw_affiliation_string":"Contribution as Domain Experts, KPI Generation and Ground Truth formulation","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Saravanan Balasubramanian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saravanan Balasubramanian","raw_affiliation_strings":["Contribution as Domain Experts, KPI Generation and Ground Truth formulation"],"affiliations":[{"raw_affiliation_string":"Contribution as Domain Experts, KPI Generation and Ground Truth formulation","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054186805","display_name":"Seema Pareek","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seema Pareek","raw_affiliation_strings":["Contribution as Domain Experts, KPI Generation and Ground Truth formulation"],"affiliations":[{"raw_affiliation_string":"Contribution as Domain Experts, KPI Generation and Ground Truth formulation","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087689674","display_name":"Shwetha Shwetha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shwetha","raw_affiliation_strings":["Primary Contributor for Time Series Analysis"],"affiliations":[{"raw_affiliation_string":"Primary Contributor for Time Series Analysis","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5051372626"],"corresponding_institution_ids":["https://openalex.org/I4210139030"],"apc_list":null,"apc_paid":null,"fwci":1.6184,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.84795144,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9998999834060669,"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":0.9998999834060669,"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/T11644","display_name":"Spam and Phishing Detection","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.996999979019165,"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/computer-science","display_name":"Computer science","score":0.6265469193458557},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6179454326629639},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.19233736395835876}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6265469193458557},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6179454326629639},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.19233736395835876}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wcncw48565.2020.9124843","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcncw48565.2020.9124843","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W2047803769","https://openalex.org/W2154358621","https://openalex.org/W2182316912","https://openalex.org/W2405555754","https://openalex.org/W2561284510","https://openalex.org/W2798058877","https://openalex.org/W2962736999","https://openalex.org/W3015379812","https://openalex.org/W6631190155","https://openalex.org/W6713525512"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"With":[0],"the":[1,8,43,84,95,158,196,199],"widespread":[2],"usage":[3],"of":[4,11,42,87,97,103,111,149,160],"4G":[5],"technologies":[6],"and":[7,23,31,53,73,151,198],"upcoming":[9],"promise":[10],"5G":[12],"networks,":[13],"there":[14],"is":[15,48,174,205],"a":[16,49,66,77,81,88,98,138,161,180],"strong":[17],"need":[18,67],"for":[19,56,68,166,186],"increased":[20],"network":[21,38,44,100,112,125,201],"performance":[22,105],"reliability.":[24],"However,":[25],"as":[26],"these":[27],"networks":[28],"become":[29],"bigger":[30],"faster,":[32],"so":[33],"does":[34],"their":[35],"complexity.":[36],"Currently,":[37],"operators":[39],"detect":[40],"most":[41],"failures":[45],"manually.":[46],"This":[47],"very":[50],"time":[51,229],"consuming":[52],"tedious":[54],"task":[55],"them,":[57],"oftentimes":[58],"taking":[59],"up":[60],"to":[61,194],"several":[62],"hours.":[63],"Thereby":[64],"arises":[65],"an":[69,171],"automated":[70],"Anomaly":[71],"Detection":[72],"Correction":[74],"system.":[75],"Such":[76],"system":[78],"would":[79],"be":[80,128],"step":[82],"towards":[83],"ultimate":[85],"goal":[86],"cognitive":[89],"self-organizing":[90],"network.":[91],"We":[92,155,190],"here":[93],"take":[94],"case":[96],"mobile":[99],"with":[101],"hundreds":[102],"key":[104],"indicators,":[106],"which":[107,145],"generates":[108],"huge":[109],"amount":[110],"logs":[113],"every":[114],"hour.":[115],"Since":[116],"user":[117],"behavior":[118,204],"has":[119],"patterns":[120],"in":[121,153,176,226],"usage,":[122],"e.g.":[123],"weekdays":[124],"traffic":[126,132],"will":[127],"higher":[129],"than":[130],"weekend\u00e2\u20ac\u2122s":[131],"near":[133],"office":[134],"areas,":[135],"we":[136,178],"analyze":[137],"Time":[139,212],"Series":[140,213],"(TS)":[141],"Decomposition":[142,214],"based":[143,183],"approach,":[144,184],"takes":[146],"into":[147],"consideration":[148],"trends":[150],"seasonality":[152],"data.":[154],"also":[156],"explore":[157],"use":[159],"seasonal":[162],"auto-regressive":[163],"technique,":[164],"SARIMA,":[165],"anomaly":[167,220],"detection.":[168],"Assuming":[169],"that":[170,210],"anomalous":[172,228],"behaviour":[173],"continuous":[175],"time,":[177],"evaluate":[179],"recurrent":[181],"encoder-decoder":[182],"MSCRED":[185,222],"Anomalous":[187],"Window":[188],"Detection.":[189],"do":[191],"this":[192],"analysis":[193],"find":[195],"KPI":[197],"respective":[200],"element,":[202],"whose":[203],"abnormal.":[206],"Our":[207],"results":[208],"show":[209],"while":[211],"outperforms":[215],"SARIMA":[216],"over":[217],"single":[218],"point":[219],"detection,":[221],"significantly":[223],"performs":[224],"well":[225],"predicting":[227],"windows.":[230]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1}],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2025-10-10T00:00:00"}
