{"id":"https://openalex.org/W4312376538","doi":"https://doi.org/10.1109/icrito56286.2022.9964756","title":"Detection of Intrusions using Support Vector Machines and Deep Neural Networks","display_name":"Detection of Intrusions using Support Vector Machines and Deep Neural Networks","publication_year":2022,"publication_date":"2022-10-13","ids":{"openalex":"https://openalex.org/W4312376538","doi":"https://doi.org/10.1109/icrito56286.2022.9964756"},"language":"en","primary_location":{"id":"doi:10.1109/icrito56286.2022.9964756","doi":"https://doi.org/10.1109/icrito56286.2022.9964756","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icrito56286.2022.9964756","pdf_url":null,"source":{"id":"https://openalex.org/S4363607923","display_name":"International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","raw_type":"proceedings-article"},"parent_works":[],"lineage":["https://openalex.org/W4312376538"],"child_works_count":0,"type":"conference-paper","is_traditional":true,"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/A5071242462","display_name":"N. D. Patel","orcid":"https://orcid.org/0000-0002-8098-7110","observed_orcids":["https://orcid.org/0000-0002-8098-7110"]},"institutions":[{"id":"https://openalex.org/I36893310","display_name":"University of Hyderabad","ror":"https://ror.org/04a7rxb17","country_code":"IN","type":"education","lineage":["https://openalex.org/I36893310"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"N D Patel","raw_affiliation_strings":["University of Hyderabad Gachibowli,SCIS,Hyderabad,India,500046"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Hyderabad Gachibowli,SCIS,Hyderabad,India,500046","institution_ids":["https://openalex.org/I36893310"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112070114","display_name":"Babu M. Mehtre","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I150312865","display_name":"Institute for Development and Research in Banking Technology","ror":"https://ror.org/00ta0g865","country_code":"IN","type":"government","lineage":["https://openalex.org/I150312865"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"B M Mehtre","raw_affiliation_strings":["CoECS, IDRBT, Hyderabad, TS, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CoECS, IDRBT, Hyderabad, TS, India","institution_ids":["https://openalex.org/I150312865"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045555361","display_name":"Rajeev Wankar","orcid":"https://orcid.org/0000-0003-3714-1619","observed_orcids":["https://orcid.org/0000-0003-3714-1619"]},"institutions":[{"id":"https://openalex.org/I36893310","display_name":"University of Hyderabad","ror":"https://ror.org/04a7rxb17","country_code":"IN","type":"education","lineage":["https://openalex.org/I36893310"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rajeev Wankar","raw_affiliation_strings":["University of Hyderabad,SCIS,Hyderabad,TS,India","SCIS, University of Hyderabad, Hyderabad, TS, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Hyderabad,SCIS,Hyderabad,TS,India","institution_ids":["https://openalex.org/I36893310"]},{"raw_affiliation_string":"SCIS, University of Hyderabad, Hyderabad, TS, India","institution_ids":["https://openalex.org/I36893310"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.955,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.86241611,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"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":0.9990000128746033,"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":0.9990000128746033,"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/T12326","display_name":"Network Packet Processing and Optimization","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T12391","display_name":"Artificial Immune Systems Applications","score":9.999999747378752e-05,"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/intrusion-detection","display_name":"intrusion detection","score":0.8769999742507935},{"id":"https://openalex.org/keywords/network-intrusion-detection","display_name":"network intrusion detection","score":0.753000020980835},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.9369999766349792},{"id":"https://openalex.org/keywords/deep-neural-network","display_name":"deep neural network","score":1.0},{"id":"https://openalex.org/keywords/nsl-kdd","display_name":"NSL-KDD dataset","score":0.9210000038146973},{"id":"https://openalex.org/keywords/classification-accuracy","display_name":"classification accuracy","score":0.44600000977516174},{"id":"https://openalex.org/keywords/false-positive-reduction","display_name":"false positive reduction","score":0.5120000243186951},{"id":"https://openalex.org/keywords/network-traffic-analysis","display_name":"network traffic analysis","score":0.5950000286102295}],"concepts":[{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.8668757677078247},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.8319494724273682},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7408297061920166},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7241901755332947},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6267749071121216},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5958256125450134},{"id":"https://openalex.org/C137524506","wikidata":"https://www.wikidata.org/wiki/Q2247688","display_name":"Anomaly-based intrusion detection system","level":3,"score":0.5597307682037354},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.5506139397621155},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49923157691955566},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4248698353767395}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icrito56286.2022.9964756","doi":"https://doi.org/10.1109/icrito56286.2022.9964756","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icrito56286.2022.9964756","pdf_url":null,"source":{"id":"https://openalex.org/S4363607923","display_name":"International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W189008923","https://openalex.org/W1495061682","https://openalex.org/W1995678176","https://openalex.org/W2099940443","https://openalex.org/W2119256650","https://openalex.org/W2128728535","https://openalex.org/W2147614625","https://openalex.org/W2191495690","https://openalex.org/W2287408518","https://openalex.org/W2783380896","https://openalex.org/W2904332864","https://openalex.org/W2915197513","https://openalex.org/W2955739827","https://openalex.org/W2965857035","https://openalex.org/W2997414109","https://openalex.org/W3001567147","https://openalex.org/W3089153145","https://openalex.org/W3107566242","https://openalex.org/W4206146936","https://openalex.org/W4239510810"],"related_works":["https://openalex.org/W2607252931","https://openalex.org/W7117530176","https://openalex.org/W2885565273","https://openalex.org/W4226293161","https://openalex.org/W3115557710","https://openalex.org/W4411446951","https://openalex.org/W7214755848","https://openalex.org/W2324816571","https://openalex.org/W2409097990","https://openalex.org/W3026705323"],"abstract_inverted_index":{"An":[0],"Intrusion":[1],"detection":[2,11,18,51,110,114,162],"system":[3],"(IDS)":[4],"plays":[5],"a":[6,65,70,77],"role":[7],"in":[8,108,160],"network":[9,13,44,80,87],"intrusion":[10,109,135,161],"through":[12],"data":[14,35,45,59,138,148],"analysis,":[15],"and":[16,21,37,92,96,101],"high":[17],"accuracy,":[19,90],"precision,":[20,91],"recall":[22,93],"are":[23,41],"required":[24],"to":[25,111,155],"detect":[26],"intrusions.":[27],"Also,":[28],"various":[29],"techniques":[30],"such":[31],"as":[32,136,149],"expert":[33],"systems,":[34],"mining,":[36],"state":[38],"transition":[39],"analysis":[40],"used":[42,107],"for":[43],"analysis.":[46],"The":[47,61,89,129],"paper":[48],"compares":[49],"the":[50,54,74,84,113,117,126,143],"effects":[52,115],"of":[53,83,116,131,145],"two":[55,118],"IDS":[56],"methods":[57],"using":[58,98],"mining.":[60],"first":[62],"technique":[63],"is":[64,76,105,139],"support":[66],"vector":[67],"machine":[68,71],"(SVM),":[69],"learning":[72],"algorithm;":[73],"second":[75],"deep":[78],"neural":[79,86],"(DNN),":[81],"one":[82],"artificial":[85],"models.":[88],"were":[94],"calculated":[95],"compared":[97],"NSL-KDD":[99],"training":[100],"validation":[102],"data,":[103],"which":[104],"widely":[106],"compare":[112],"techniques.":[119],"DNN":[120,153],"shows":[121],"slightly":[122],"higher":[123],"accuracy":[124],"than":[125,142,163],"SVM":[127],"model.":[128],"risk":[130,144],"recognizing":[132],"an":[133,150],"actual":[134],"normal":[137,147],"much":[140,157],"greater":[141],"considering":[146],"intrusion,":[151],"so":[152],"proves":[154],"be":[156],"more":[158],"effective":[159],"SVM.":[164]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3}],"updated_date":"2026-11-14T11:04:58.165337","created_date":"2023-01-04T00:00:00"}