{"id":"https://openalex.org/W2775103799","doi":"https://doi.org/10.1109/icacci.2017.8126009","title":"Applying convolutional neural network for network intrusion detection","display_name":"Applying convolutional neural network for network intrusion detection","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2775103799","doi":"https://doi.org/10.1109/icacci.2017.8126009","mag":"2775103799"},"language":"en","primary_location":{"id":"doi:10.1109/icacci.2017.8126009","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icacci.2017.8126009","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI)","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/A5112825787","display_name":"R. Vinayakumar","orcid":null},"institutions":[{"id":"https://openalex.org/I81556334","display_name":"Amrita Vishwa Vidyapeetham","ror":"https://ror.org/03am10p12","country_code":"IN","type":"education","lineage":["https://openalex.org/I81556334"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"R Vinayakumar","raw_affiliation_strings":["Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore"],"affiliations":[{"raw_affiliation_string":"Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore","institution_ids":["https://openalex.org/I81556334"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113113768","display_name":"K. P. Soman","orcid":null},"institutions":[{"id":"https://openalex.org/I81556334","display_name":"Amrita Vishwa Vidyapeetham","ror":"https://ror.org/03am10p12","country_code":"IN","type":"education","lineage":["https://openalex.org/I81556334"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"K P Soman","raw_affiliation_strings":["Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore"],"affiliations":[{"raw_affiliation_string":"Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore","institution_ids":["https://openalex.org/I81556334"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029900047","display_name":"Prabaharan Poornachandran","orcid":null},"institutions":[{"id":"https://openalex.org/I81556334","display_name":"Amrita Vishwa Vidyapeetham","ror":"https://ror.org/03am10p12","country_code":"IN","type":"education","lineage":["https://openalex.org/I81556334"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Prabaharan Poornachandran","raw_affiliation_strings":["Center for Cyber Security Systems and Networks, Amrita University, India"],"affiliations":[{"raw_affiliation_string":"Center for Cyber Security Systems and Networks, Amrita University, India","institution_ids":["https://openalex.org/I81556334"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5112825787"],"corresponding_institution_ids":["https://openalex.org/I81556334"],"apc_list":null,"apc_paid":null,"fwci":29.3806,"has_fulltext":false,"cited_by_count":524,"citation_normalized_percentile":{"value":0.99757228,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1222","last_page":"1228"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9990000128746033,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9987999796867371,"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/computer-science","display_name":"Computer science","score":0.8419187068939209},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7860888242721558},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6921384334564209},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.64149010181427},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5761449337005615},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.4434935748577118},{"id":"https://openalex.org/keywords/network-topology","display_name":"Network topology","score":0.422306627035141},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39364904165267944},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3388274908065796},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3152841031551361},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.23509037494659424}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8419187068939209},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7860888242721558},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6921384334564209},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.64149010181427},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5761449337005615},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.4434935748577118},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.422306627035141},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39364904165267944},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3388274908065796},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3152841031551361},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.23509037494659424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icacci.2017.8126009","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icacci.2017.8126009","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W169539560","https://openalex.org/W1544101352","https://openalex.org/W1551469486","https://openalex.org/W1598796236","https://openalex.org/W1800991598","https://openalex.org/W1811853421","https://openalex.org/W1832693441","https://openalex.org/W1851171594","https://openalex.org/W2064675550","https://openalex.org/W2088476668","https://openalex.org/W2099940443","https://openalex.org/W2110485445","https://openalex.org/W2130605126","https://openalex.org/W2157331557","https://openalex.org/W2160598920","https://openalex.org/W2187089797","https://openalex.org/W2215862420","https://openalex.org/W2293634267","https://openalex.org/W2399941526","https://openalex.org/W2402144811","https://openalex.org/W2465031466","https://openalex.org/W2529525882","https://openalex.org/W2548971154","https://openalex.org/W2557513839","https://openalex.org/W2576897537","https://openalex.org/W2953384591","https://openalex.org/W2962851944","https://openalex.org/W3214373139","https://openalex.org/W4245460974","https://openalex.org/W4254816979","https://openalex.org/W6606918967","https://openalex.org/W6635679246","https://openalex.org/W6679248074","https://openalex.org/W6696934422","https://openalex.org/W6713134421","https://openalex.org/W6719627428","https://openalex.org/W6803782233"],"related_works":["https://openalex.org/W4225394202","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W1847088711","https://openalex.org/W3036642985","https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3008584592"],"abstract_inverted_index":{"Recently,":[0],"Convolutional":[1],"neural":[2,65],"network":[3,36,66,84,111,126,129,196],"(CNN)":[4],"architectures":[5,155],"in":[6,13,30,49,135,147,160],"deep":[7],"learning":[8,56,145,166],"have":[9,156,177],"achieved":[10],"significant":[11],"results":[12],"the":[14,24,88,96,109,148,163,173,187],"field":[15],"of":[16,26,79,90,115,190,195],"computer":[17],"vision.":[18],"To":[19,86,107],"transform":[20],"this":[21,33],"performance":[22],"toward":[23],"task":[25],"intrusion":[27],"detection":[28],"(ID)":[29],"cyber":[31],"security,":[32],"paper":[34],"models":[35,134],"traffic":[37,197],"as":[38,59,104],"time-series,":[39],"particularly":[40],"transmission":[41],"control":[42],"protocol":[43,46],"/":[44],"internet":[45],"(TCP/IP)":[47],"packets":[48],"a":[50],"predefined":[51],"time":[52],"range":[53,149],"with":[54,123,144],"supervised":[55],"methods":[57],"such":[58,103],"multi-layer":[60],"perceptron":[61],"(MLP),":[62],"CNN,":[63,118],"CNN-recurrent":[64],"(CNN-RNN),":[67],"CNN-long":[68],"short-term":[69],"memory":[70],"(CNN-LSTM)":[71],"and":[72,82,121,128,152],"CNN-gated":[73],"recurrent":[74],"unit":[75],"(GRU),":[76],"using":[77],"millions":[78],"known":[80],"good":[81],"bad":[83],"connections.":[85,198],"measure":[87],"efficacy":[89],"these":[91],"approaches":[92],"we":[93],"evaluate":[94],"on":[95],"most":[97],"important":[98],"synthetic":[99],"ID":[100],"data":[101],"set":[102],"KDDCup":[105],"99.":[106],"select":[108],"optimal":[110],"architecture,":[112],"comprehensive":[113],"analysis":[114],"various":[116],"MLP,":[117],"CNN-RNN,":[119],"CNN-LSTM":[120],"CNN-GRU":[122],"its":[124,153],"topologies,":[125],"parameters":[127],"structures":[130],"is":[131,169],"used.":[132],"The":[133],"each":[136],"experiment":[137],"are":[138],"run":[139],"up":[140],"to":[141,162,172,179],"1000":[142],"epochs":[143],"rate":[146],"[0.01-05].":[150],"CNN":[151,176],"variant":[154],"significantly":[157],"performed":[158],"well":[159],"comparison":[161],"classical":[164],"machine":[165],"classifiers.":[167],"This":[168],"mainly":[170],"due":[171],"reason":[174],"that":[175,185],"capability":[178],"extract":[180],"high":[181],"level":[182,192],"feature":[183,193],"representations":[184],"represents":[186],"abstract":[188],"form":[189],"low":[191],"sets":[194]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":53},{"year":2024,"cited_by_count":80},{"year":2023,"cited_by_count":76},{"year":2022,"cited_by_count":76},{"year":2021,"cited_by_count":101},{"year":2020,"cited_by_count":77},{"year":2019,"cited_by_count":39},{"year":2018,"cited_by_count":18}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}