{"id":"https://openalex.org/W2989622188","doi":"https://doi.org/10.1109/isncc.2019.8909140","title":"Efficient Network Intrusion Detection Using PCA-Based Dimensionality Reduction of Features","display_name":"Efficient Network Intrusion Detection Using PCA-Based Dimensionality Reduction of Features","publication_year":2019,"publication_date":"2019-06-01","ids":{"openalex":"https://openalex.org/W2989622188","doi":"https://doi.org/10.1109/isncc.2019.8909140","mag":"2989622188"},"language":"en","primary_location":{"id":"doi:10.1109/isncc.2019.8909140","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isncc.2019.8909140","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Symposium on Networks, Computers and Communications (ISNCC)","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/A5024287768","display_name":"Razan Abdulhammed","orcid":"https://orcid.org/0000-0002-5939-6009"},"institutions":[{"id":"https://openalex.org/I154300980","display_name":"University of Bridgeport","ror":"https://ror.org/01rf3yp57","country_code":"US","type":"education","lineage":["https://openalex.org/I154300980"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Razan Abdulhammed","raw_affiliation_strings":["School of Engineering, University of Bridgeport, Bridgeport, CT, USA"],"affiliations":[{"raw_affiliation_string":"School of Engineering, University of Bridgeport, Bridgeport, CT, USA","institution_ids":["https://openalex.org/I154300980"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031601451","display_name":"Miad Faezipour","orcid":"https://orcid.org/0000-0003-2684-0887"},"institutions":[{"id":"https://openalex.org/I154300980","display_name":"University of Bridgeport","ror":"https://ror.org/01rf3yp57","country_code":"US","type":"education","lineage":["https://openalex.org/I154300980"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Miad Faezipour","raw_affiliation_strings":["School of Engineering, University of Bridgeport, Bridgeport, CT, USA"],"affiliations":[{"raw_affiliation_string":"School of Engineering, University of Bridgeport, Bridgeport, CT, USA","institution_ids":["https://openalex.org/I154300980"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018990690","display_name":"Hassan Musafer","orcid":"https://orcid.org/0000-0003-1939-0224"},"institutions":[{"id":"https://openalex.org/I154300980","display_name":"University of Bridgeport","ror":"https://ror.org/01rf3yp57","country_code":"US","type":"education","lineage":["https://openalex.org/I154300980"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hassan Musafer","raw_affiliation_strings":["School of Engineering, University of Bridgeport, Bridgeport, CT, USA"],"affiliations":[{"raw_affiliation_string":"School of Engineering, University of Bridgeport, Bridgeport, CT, USA","institution_ids":["https://openalex.org/I154300980"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045161880","display_name":"Abdelshakour Abuzneid","orcid":"https://orcid.org/0000-0001-5416-0315"},"institutions":[{"id":"https://openalex.org/I154300980","display_name":"University of Bridgeport","ror":"https://ror.org/01rf3yp57","country_code":"US","type":"education","lineage":["https://openalex.org/I154300980"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abdelshakour Abuzneid","raw_affiliation_strings":["School of Engineering, University of Bridgeport, Bridgeport, CT, USA"],"affiliations":[{"raw_affiliation_string":"School of Engineering, University of Bridgeport, Bridgeport, CT, USA","institution_ids":["https://openalex.org/I154300980"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5024287768"],"corresponding_institution_ids":["https://openalex.org/I154300980"],"apc_list":null,"apc_paid":null,"fwci":2.4143,"has_fulltext":false,"cited_by_count":50,"citation_normalized_percentile":{"value":0.90124657,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.9994000196456909,"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":0.9975000023841858,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7581397294998169},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.7091753482818604},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.678703248500824},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6706794500350952},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6487358808517456},{"id":"https://openalex.org/keywords/quadratic-classifier","display_name":"Quadratic classifier","score":0.5621293187141418},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.5536330342292786},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.5336936712265015},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46471843123435974},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4552423655986786},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4547334313392639},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4496748745441437},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.43619656562805176}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7581397294998169},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.7091753482818604},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.678703248500824},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6706794500350952},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6487358808517456},{"id":"https://openalex.org/C52620605","wikidata":"https://www.wikidata.org/wiki/Q7268357","display_name":"Quadratic classifier","level":3,"score":0.5621293187141418},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.5536330342292786},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.5336936712265015},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46471843123435974},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4552423655986786},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4547334313392639},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4496748745441437},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.43619656562805176}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isncc.2019.8909140","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isncc.2019.8909140","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Symposium on Networks, Computers and Communications (ISNCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6800000071525574,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W2002394060","https://openalex.org/W2028070713","https://openalex.org/W2031163547","https://openalex.org/W2038765014","https://openalex.org/W2063151808","https://openalex.org/W2090135786","https://openalex.org/W2155461860","https://openalex.org/W2159241419","https://openalex.org/W2462597051","https://openalex.org/W2768426510","https://openalex.org/W2780584094","https://openalex.org/W2789828921","https://openalex.org/W2834705605","https://openalex.org/W2888557253","https://openalex.org/W2891833507","https://openalex.org/W2896412072","https://openalex.org/W2899191278","https://openalex.org/W2899653275","https://openalex.org/W2900678839","https://openalex.org/W2911278693","https://openalex.org/W2913446593","https://openalex.org/W2921708219","https://openalex.org/W4244472745","https://openalex.org/W6753029567","https://openalex.org/W6800561921"],"related_works":["https://openalex.org/W2113454941","https://openalex.org/W2951122819","https://openalex.org/W4288315282","https://openalex.org/W2794812819","https://openalex.org/W2114217318","https://openalex.org/W2411585343","https://openalex.org/W2587881214","https://openalex.org/W3104072235","https://openalex.org/W2753804328","https://openalex.org/W2085106164"],"abstract_inverted_index":{"Designing":[0],"a":[1,39,80,149,175,215],"machine":[2],"learning":[3],"based":[4,178],"network":[5,31,194],"intrusion":[6,195],"detection":[7],"system":[8],"(IDS)":[9],"with":[10,33,119,157],"high-dimensional":[11],"features":[12,23,88,121],"can":[13,24],"lead":[14],"to":[15,61,92,159,181,200,209],"prolonged":[16],"classification":[17,29,126],"processes.":[18,27],"This":[19,71],"is":[20],"while":[21,213],"low-dimensional":[22,87,120],"reduce":[25,201],"these":[26],"Moreover,":[28],"of":[30,53,68,132,186,218],"traffic":[32],"imbalanced":[34,54,184],"class":[35,160,168,189],"distributions":[36],"has":[37],"posed":[38],"significant":[40],"drawback":[41],"on":[42],"the":[43,51,56,66,69,183,187,192,202],"performance":[44,67,129,152],"attainable":[45],"by":[46],"most":[47],"well-known":[48],"classifiers.":[49],"With":[50],"presence":[52],"data,":[55],"known":[57],"metrics":[58],"may":[59],"fail":[60],"provide":[62],"adequate":[63],"information":[64],"about":[65],"classifier.":[70],"study":[72],"first":[73],"uses":[74],"Principal":[75],"Component":[76],"Analysis":[77,105,110],"(PCA)":[78],"as":[79,97],"feature":[81,205],"dimensionality":[82],"reduction":[83],"approach.":[84],"The":[85,116],"resulting":[86],"are":[89],"then":[90],"used":[91],"build":[93],"various":[94],"classifiers":[95],"such":[96],"Random":[98],"Forest":[99],"(RF),":[100],"Bayesian":[101],"Network,":[102],"Linear":[103],"Discriminant":[104,109],"(LDA)":[106],"and":[107,124,141,167,222],"Quadratic":[108],"(QDA)":[111],"for":[112],"designing":[113],"an":[114],"IDS.":[115],"experimental":[117],"findings":[118],"in":[122,130,144,191,220],"binary":[123,223],"multi-class":[125,221],"show":[127],"better":[128],"terms":[131],"Detection":[133],"Rate":[134,139],"(DR),":[135],"F-Measure,":[136],"False":[137],"Alarm":[138],"(FAR),":[140],"Accuracy.":[142],"Furthermore,":[143],"this":[145],"paper,":[146],"we":[147,173],"apply":[148],"Multi-Class":[150],"Combined":[151],"metric":[153],"Combi":[154],"ned":[155],"Mc":[156],"respect":[158],"distribution":[161,169,177,185],"through":[162],"incorporating":[163],"FAR,":[164],"DR,":[165],"Accuracy,":[166],"parameters.":[170],"In":[171],"addition,":[172],"developed":[174],"uniform":[176],"balancing":[179],"approach":[180],"handle":[182],"minority":[188],"instances":[190],"CICIDS2017":[193,203],"dataset.":[196],"We":[197],"were":[198],"able":[199],"dataset's":[204],"dimensions":[206],"from":[207],"81":[208],"10":[210],"using":[211],"PCA,":[212],"maintaining":[214],"high":[216],"accuracy":[217],"99.6%":[219],"classification.":[224]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":4},{"year":2018,"cited_by_count":1}],"updated_date":"2026-04-11T08:14:18.477133","created_date":"2025-10-10T00:00:00"}