{"id":"https://openalex.org/W3081055701","doi":"https://doi.org/10.4018/ijec.2020100106","title":"EFS-LSTM (Ensemble-Based Feature Selection With LSTM) Classifier for Intrusion Detection System","display_name":"EFS-LSTM (Ensemble-Based Feature Selection With LSTM) Classifier for Intrusion Detection System","publication_year":2020,"publication_date":"2020-08-26","ids":{"openalex":"https://openalex.org/W3081055701","doi":"https://doi.org/10.4018/ijec.2020100106","mag":"3081055701"},"language":"en","primary_location":{"id":"doi:10.4018/ijec.2020100106","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijec.2020100106","pdf_url":null,"source":{"id":"https://openalex.org/S129960573","display_name":"International Journal of e-Collaboration","issn_l":"1548-3673","issn":["1548-3673","1548-3681"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of e-Collaboration","raw_type":"journal-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/A5103274727","display_name":"D Preethi","orcid":null},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Preethi D.","raw_affiliation_strings":["Vellore Institute of Technology, India"],"affiliations":[{"raw_affiliation_string":"Vellore Institute of Technology, India","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084945799","display_name":"Neelu Khare","orcid":"https://orcid.org/0000-0001-9516-0637"},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Neelu Khare","raw_affiliation_strings":["Vellore Institute of Technology, India"],"affiliations":[{"raw_affiliation_string":"Vellore Institute of Technology, India","institution_ids":["https://openalex.org/I876193797"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103274727"],"corresponding_institution_ids":["https://openalex.org/I876193797"],"apc_list":null,"apc_paid":null,"fwci":0.9251,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.77124106,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"16","issue":"4","first_page":"72","last_page":"86"},"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":0.9980999827384949,"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":0.9957000017166138,"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/feature-selection","display_name":"Feature selection","score":0.769843578338623},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7568975687026978},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7344350814819336},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6044989824295044},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.5707911849021912},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.5431531667709351},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5219413638114929},{"id":"https://openalex.org/keywords/information-gain","display_name":"Information gain","score":0.4473263621330261},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4458619952201843},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44187384843826294}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.769843578338623},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7568975687026978},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7344350814819336},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6044989824295044},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.5707911849021912},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.5431531667709351},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5219413638114929},{"id":"https://openalex.org/C2983203078","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Information gain","level":2,"score":0.4473263621330261},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4458619952201843},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44187384843826294},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"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:10.4018/ijec.2020100106","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijec.2020100106","pdf_url":null,"source":{"id":"https://openalex.org/S129960573","display_name":"International Journal of e-Collaboration","issn_l":"1548-3673","issn":["1548-3673","1548-3681"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of e-Collaboration","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1570448133","https://openalex.org/W1811853421","https://openalex.org/W2064675550","https://openalex.org/W2099940443","https://openalex.org/W2140190241","https://openalex.org/W2335999708","https://openalex.org/W2346331195","https://openalex.org/W2512144135","https://openalex.org/W2732383329","https://openalex.org/W2747715470","https://openalex.org/W2762776925","https://openalex.org/W2776074343","https://openalex.org/W2804368608","https://openalex.org/W2807319534","https://openalex.org/W2807601763","https://openalex.org/W2889109290","https://openalex.org/W2893275956","https://openalex.org/W2895787535","https://openalex.org/W2898670746","https://openalex.org/W2919115771","https://openalex.org/W6631190155"],"related_works":["https://openalex.org/W2094189286","https://openalex.org/W4237536247","https://openalex.org/W2328484534","https://openalex.org/W16133775","https://openalex.org/W2465988918","https://openalex.org/W2367691850","https://openalex.org/W2360805930","https://openalex.org/W4237294917","https://openalex.org/W4308273529","https://openalex.org/W2589811497"],"abstract_inverted_index":{"In":[0],"this":[1],"article,":[2],"an":[3],"EFS-LSTM,":[4],"a":[5,123],"deep":[6],"recurrent":[7],"learning":[8],"model,":[9],"is":[10,77],"proposed":[11],"for":[12,31],"network":[13,35],"intrusion":[14],"detection":[15,125],"systems.":[16],"The":[17,37,58,74,109],"EFS-LSTM":[18,75,115],"model":[19,116],"uses":[20],"ensemble-based":[21],"feature":[22,41,51,56,94,102],"selection":[23,42,95,103],"(EFS)":[24],"and":[25,53,67,72,84,105,126],"LSTM":[26],"(Long":[27],"Short":[28],"Term":[29],"Memory)":[30],"the":[32,63,80,89,114],"classification":[33,81],"of":[34],"intrusions.":[36],"EFS":[38],"combines":[39],"five":[40],"mechanisms":[43],"namely,":[44],"information":[45],"gain,":[46],"gain":[47],"ratio,":[48],"chi-square,":[49],"correlation-based":[50],"selection,":[52],"symmetric":[54],"uncertainty-based":[55],"selection.":[57],"experiments":[59],"were":[60],"conducted":[61],"using":[62,69,79,107],"benchmark":[64],"NSL-KDD":[65],"dataset":[66],"implemented":[68],"Tensor":[70],"flow":[71],"python.":[73],"classifier":[76],"evaluated":[78],"performance":[82,110],"metrics":[83],"also":[85],"compared":[86],"with":[87,99,119,122],"all":[88],"41":[90],"features":[91],"without":[92],"any":[93],"as":[96,98],"well":[97],"each":[100],"individual":[101],"techniques":[104],"classified":[106],"LSTM.":[108],"study":[111],"showed":[112],"that":[113],"outperforms":[117],"better":[118],"99.8%":[120],"accuracy":[121],"higher":[124],"less":[127],"false":[128],"alarm":[129],"rates.":[130]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
