{"id":"https://openalex.org/W2942702508","doi":"https://doi.org/10.1145/3297280.3297396","title":"A scalable and accurate feature representation method for identifying malicious mobile applications","display_name":"A scalable and accurate feature representation method for identifying malicious mobile applications","publication_year":2019,"publication_date":"2019-04-08","ids":{"openalex":"https://openalex.org/W2942702508","doi":"https://doi.org/10.1145/3297280.3297396","mag":"2942702508"},"language":"en","primary_location":{"id":"doi:10.1145/3297280.3297396","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3297280.3297396","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3297280.3297396","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3297280.3297396","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112302757","display_name":"Bo Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I90023481","display_name":"National Institute of Information and Communications Technology","ror":"https://ror.org/016bgq349","country_code":"JP","type":"facility","lineage":["https://openalex.org/I90023481"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Bo Sun","raw_affiliation_strings":["National Institute of Information and Communications Technology, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"National Institute of Information and Communications Technology, Tokyo, Japan","institution_ids":["https://openalex.org/I90023481"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014860606","display_name":"Tao Ban","orcid":"https://orcid.org/0000-0002-9616-3212"},"institutions":[{"id":"https://openalex.org/I90023481","display_name":"National Institute of Information and Communications Technology","ror":"https://ror.org/016bgq349","country_code":"JP","type":"facility","lineage":["https://openalex.org/I90023481"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tao Ban","raw_affiliation_strings":["National Institute of Information and Communications Technology, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"National Institute of Information and Communications Technology, Tokyo, Japan","institution_ids":["https://openalex.org/I90023481"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046120781","display_name":"Shun\u2010Chieh Chang","orcid":null},"institutions":[{"id":"https://openalex.org/I3141939062","display_name":"Institute for Information Industry","ror":"https://ror.org/01d8kr740","country_code":"TW","type":"nonprofit","lineage":["https://openalex.org/I3141939062"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Shun-Chieh Chang","raw_affiliation_strings":["Institute for Information Industry, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Institute for Information Industry, Taipei, Taiwan","institution_ids":["https://openalex.org/I3141939062"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080389605","display_name":"Yeali S. Sun","orcid":"https://orcid.org/0000-0002-9533-098X"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yeali S. Sun","raw_affiliation_strings":["National Taiwan University, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"National Taiwan University, Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029032117","display_name":"Takeshi Takahashi","orcid":"https://orcid.org/0000-0002-6477-7770"},"institutions":[{"id":"https://openalex.org/I90023481","display_name":"National Institute of Information and Communications Technology","ror":"https://ror.org/016bgq349","country_code":"JP","type":"facility","lineage":["https://openalex.org/I90023481"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takeshi Takahashi","raw_affiliation_strings":["National Institute of Information and Communications Technology, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"National Institute of Information and Communications Technology, Tokyo, Japan","institution_ids":["https://openalex.org/I90023481"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071687365","display_name":"Daisuke Inoue","orcid":"https://orcid.org/0000-0002-4373-0834"},"institutions":[{"id":"https://openalex.org/I90023481","display_name":"National Institute of Information and Communications Technology","ror":"https://ror.org/016bgq349","country_code":"JP","type":"facility","lineage":["https://openalex.org/I90023481"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Daisuke Inoue","raw_affiliation_strings":["National Institute of Information and Communications Technology, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"National Institute of Information and Communications Technology, Tokyo, Japan","institution_ids":["https://openalex.org/I90023481"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5112302757"],"corresponding_institution_ids":["https://openalex.org/I90023481"],"apc_list":null,"apc_paid":null,"fwci":0.8293,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.72014238,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1182","last_page":"1189"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11241","display_name":"Advanced Malware Detection Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11241","display_name":"Advanced Malware Detection Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9983000159263611,"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":0.9962000250816345,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8766814470291138},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7835684418678284},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6164373755455017},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6032208204269409},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5819918513298035},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5298464298248291},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.48759976029396057},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.45603761076927185},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.44042977690696716},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.187455415725708},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.11211952567100525}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8766814470291138},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7835684418678284},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6164373755455017},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6032208204269409},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5819918513298035},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5298464298248291},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.48759976029396057},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.45603761076927185},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.44042977690696716},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.187455415725708},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.11211952567100525},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3297280.3297396","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3297280.3297396","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3297280.3297396","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3297280.3297396","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3297280.3297396","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3297280.3297396","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.46000000834465027,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2942702508.pdf","grobid_xml":"https://content.openalex.org/works/W2942702508.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W168564468","https://openalex.org/W1832693441","https://openalex.org/W1880262756","https://openalex.org/W1943233084","https://openalex.org/W2058180826","https://openalex.org/W2077278164","https://openalex.org/W2101234009","https://openalex.org/W2110889728","https://openalex.org/W2122672392","https://openalex.org/W2127218421","https://openalex.org/W2131744502","https://openalex.org/W2141198770","https://openalex.org/W2168649891","https://openalex.org/W2324464293","https://openalex.org/W2577741565","https://openalex.org/W2586845402","https://openalex.org/W2599823825","https://openalex.org/W2809616033","https://openalex.org/W2890864056","https://openalex.org/W2949541494","https://openalex.org/W2950133940","https://openalex.org/W3034265065"],"related_works":["https://openalex.org/W2389214306","https://openalex.org/W2965083567","https://openalex.org/W4235240664","https://openalex.org/W1838576100","https://openalex.org/W2095886385","https://openalex.org/W2889616422","https://openalex.org/W2089704382","https://openalex.org/W1983399550","https://openalex.org/W3034529322","https://openalex.org/W97075385"],"abstract_inverted_index":{"With":[0],"the":[1,7,35,70,78,83,122,125,158],"dramatic":[2],"growth":[3],"in":[4,20,60,154,189],"smartphone":[5],"usage,":[6],"number":[8],"of":[9,37,72,124,129,142,178,181,191],"new":[10],"malicious":[11,18,38,74,117,126],"mobile":[12,107],"applications":[13,19,75],"has":[14],"increased":[15],"rapidly.":[16],"Identifying":[17],"large-scale":[21,57],"datasets":[22],"is":[23,53],"intensive":[24],"and":[25,50,76,96,115,184,193],"time":[26,80,162],"consuming.":[27],"Multiple":[28],"previous":[29,182],"studies":[30,183],"have":[31],"focused":[32],"on":[33],"automating":[34],"process":[36],"application":[39,108,127],"detection":[40,128],"using":[41],"machine":[42,97],"(or":[43],"deep)":[44],"learning":[45,98],"technology.":[46],"However,":[47],"a":[48,65,101,106,138,148,186],"scalable":[49],"accurate":[51],"solution":[52],"still":[54],"lacking":[55],"for":[56,81],"applications.":[58],"Therefore,":[59],"this":[61],"study,":[62],"we":[63,119,172],"propose":[64],"novel":[66],"approach":[67,89,131],"to":[68,135,166,176],"improve":[69],"accuracy":[71],"discovering":[73],"decrease":[77],"computation":[79],"processing":[82,160],"analysis.":[84],"We":[85,145],"implemented":[86],"our":[87,130,174],"proposed":[88],"combining":[90],"data":[91],"collection,":[92],"static":[93],"feature":[94],"extraction,":[95],"algorithms.":[99,156],"Using":[100],"large":[102],"dataset":[103],"collected":[104],"from":[105,133],"store":[109],"that":[110,121,147],"included":[111],"49,045":[112],"benign":[113],"samples":[114],"12,685":[116],"samples,":[118],"demonstrate":[120],"F-measure":[123],"ranges":[132],"0.968":[134],"0.995,":[136],"with":[137],"false":[139],"positive":[140],"rate":[141],"0.48%":[143],"~3.3%.":[144],"find":[146],"multi-layer":[149],"perceptron":[150],"classifier":[151],"performs":[152],"best":[153],"these":[155],"Moreover,":[157],"analysis":[159],"running":[161],"can":[163],"be":[164],"compressed":[165],"less":[167],"than":[168],"18":[169],"min.":[170],"Finally,":[171],"compare":[173],"method":[175],"those":[177],"two":[179],"types":[180],"report":[185],"better":[187],"performance":[188],"terms":[190],"scalability":[192],"accuracy.":[194]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
