{"id":"https://openalex.org/W3199055814","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533323","title":"LSTM Hyper-Parameter Selection for Malware Detection: Interaction Effects and Hierarchical Selection Approach","display_name":"LSTM Hyper-Parameter Selection for Malware Detection: Interaction Effects and Hierarchical Selection Approach","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3199055814","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533323","mag":"3199055814"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9533323","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533323","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2109.11500","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Mohit Sewak","orcid":null},"institutions":[{"id":"https://openalex.org/I4210124949","display_name":"Microsoft Research (India)","ror":"https://ror.org/02w7f3w92","country_code":"IN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210124949"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Mohit Sewak","raw_affiliation_strings":["Security & Compliance Research Microsoft R&D, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Security & Compliance Research Microsoft R&D, India","institution_ids":["https://openalex.org/I4210124949"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Sanjay K. Sahay","orcid":null},"institutions":[{"id":"https://openalex.org/I4210148827","display_name":"Birla Institute of Technology and Science, Pilani - Goa Campus","ror":"https://ror.org/046sh6j17","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210148827","https://openalex.org/I74796645"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sanjay K. Sahay","raw_affiliation_strings":["Goa Campus, BITS Pilani, Goa, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Goa Campus, BITS Pilani, Goa, India","institution_ids":["https://openalex.org/I4210148827"]}]},{"author_position":"last","author":{"id":null,"display_name":"Hemant Rathore","orcid":null},"institutions":[{"id":"https://openalex.org/I4210148827","display_name":"Birla Institute of Technology and Science, Pilani - Goa Campus","ror":"https://ror.org/046sh6j17","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210148827","https://openalex.org/I74796645"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Hemant Rathore","raw_affiliation_strings":["Goa Campus, BITS Pilani, Goa, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Goa Campus, BITS Pilani, Goa, India","institution_ids":["https://openalex.org/I4210148827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2713,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.41136329,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9994000196456909,"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":0.9994000196456909,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9994000196456909,"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/T10028","display_name":"Topic Modeling","score":0.9977999925613403,"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/hyperparameter","display_name":"Hyperparameter","score":0.5950999855995178},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5674999952316284},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.48980000615119934},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.4503999948501587},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.44589999318122864},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.4219000041484833},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.3874000012874603}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8055999875068665},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.614799976348877},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.5950999855995178},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5867000222206116},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5674999952316284},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.48980000615119934},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.4503999948501587},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.44589999318122864},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.4219000041484833},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3874000012874603},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.3709000051021576},{"id":"https://openalex.org/C541664917","wikidata":"https://www.wikidata.org/wiki/Q14001","display_name":"Malware","level":2,"score":0.34470000863075256},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.32170000672340393},{"id":"https://openalex.org/C2780806968","wikidata":"https://www.wikidata.org/wiki/Q6045196","display_name":"Interaction model","level":2,"score":0.31679999828338623},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.31529998779296875},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.31360000371932983},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C158251709","wikidata":"https://www.wikidata.org/wiki/Q354025","display_name":"Intrusion","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2734000086784363}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9533323","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533323","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2109.11500","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2109.11500","pdf_url":"https://arxiv.org/pdf/2109.11500","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2109.11500","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2109.11500","pdf_url":"https://arxiv.org/pdf/2109.11500","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1545528966","https://openalex.org/W1666731339","https://openalex.org/W1689711448","https://openalex.org/W1893133781","https://openalex.org/W2059422848","https://openalex.org/W2079215333","https://openalex.org/W2121029939","https://openalex.org/W2154137718","https://openalex.org/W2557513839","https://openalex.org/W2632775315","https://openalex.org/W2752241832","https://openalex.org/W2753669113","https://openalex.org/W2782578820","https://openalex.org/W2791879367","https://openalex.org/W2792736988","https://openalex.org/W2799908179","https://openalex.org/W2886383271","https://openalex.org/W2887787078","https://openalex.org/W2954043846","https://openalex.org/W2955343150","https://openalex.org/W3008071995","https://openalex.org/W3099091571","https://openalex.org/W3126761238","https://openalex.org/W4246528899","https://openalex.org/W6685536428","https://openalex.org/W6713098461","https://openalex.org/W6741655716","https://openalex.org/W6759983693","https://openalex.org/W6763123837","https://openalex.org/W6786990758","https://openalex.org/W6790362100","https://openalex.org/W6790735642","https://openalex.org/W6947963860"],"related_works":[],"abstract_inverted_index":{"Long-Short-Term-Memory":[0],"(LSTM)":[1],"networks":[2,16],"have":[3,17],"shown":[4],"great":[5],"promise":[6],"in":[7,32,37,43,158],"artificial":[8],"intelligence":[9],"(AI)":[10],"based":[11],"language":[12,44,135,201],"modeling.":[13],"Recently,":[14],"LSTM":[15,72,80,196],"also":[18,166],"become":[19],"popular":[20],"for":[21,74,91,108,120,124,145,171,178,180,200],"designing":[22,92,125],"an":[23,93,126,181],"AI-based":[24],"Intrusion":[25],"Detection":[26],"Systems":[27],"(IDS).":[28],"However,":[29],"its":[30,84],"applicability":[31],"IDS":[33,75,182],"is":[34,137,140],"studied":[35],"largely":[36],"the":[38,67,100,109,141,160,172,174,203,210],"default":[39],"settings":[40],"as":[41,132],"used":[42,199],"models.":[45],"Whereas":[46],"security":[47,121],"applications":[48],"offer":[49],"distinct":[50],"conditions":[51],"and":[52,76,88,105,188],"hence":[53],"warrant":[54],"careful":[55],"consideration":[56],"while":[57],"applying":[58],"such":[59],"recurrent":[60],"networks.":[61],"Therefore,":[62],"we":[63],"conducted":[64,96],"one":[65],"of":[66,99,102,112,163,212],"most":[68],"exhaustive":[69],"work":[70],"on":[71,208],"hyperparameters":[73],"experimented":[77],"with":[78],"150":[79],"configurations":[81],"to":[82,134,214],"determine":[83],"hyper-parameters'":[85],"relative-importance,":[86],"interaction-effects,":[87],"optimal":[89],"selection-approach":[90],"IDS.":[94],"We":[95,117,149,165],"multiple":[97],"analysis":[98],"results":[101],"these":[103],"experiments":[104],"empirically":[106],"controlled":[107],"interaction":[110,152],"effects":[111],"different":[113],"hyper-parameters":[114],"covariate":[115],"level.":[116],"found":[118],"that":[119,151,168],"applications,":[122],"especially":[123],"IDS,":[127],"neither":[128],"similar":[129],"relative":[130,161,176],"importance":[131,162,177],"applicable":[133],"models":[136],"valid,":[138],"nor":[139],"standard":[142],"linear":[143],"method":[144],"hyper-parameter":[146],"selection":[147],"ideal.":[148],"ascertained":[150],"effect":[153],"plays":[154],"a":[155],"crucial":[156],"role":[157],"determining":[159],"hyperparameters.":[164],"discovered":[167],"after":[169],"controlling":[170],"interaction-effect,":[173],"correct":[175],"LSTMs":[179],"are":[183,192],"batch-size,":[184],"followed-by":[185],"dropout":[186],"ratio":[187],"padding.":[189],"The":[190],"findings":[191],"significant":[193],"because":[194],"when":[195],"were":[197],"first":[198],"models,":[202],"focus":[204],"had":[205],"mostly":[206],"been":[207],"increasing":[209],"number":[211],"layers":[213],"enhance":[215],"performance.":[216]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2021-09-27T00:00:00"}