{"id":"https://openalex.org/W4309374805","doi":"https://doi.org/10.1109/smc53654.2022.9945189","title":"A Hybrid Deep Learning Method for Network Attack Prediction","display_name":"A Hybrid Deep Learning Method for Network Attack Prediction","publication_year":2022,"publication_date":"2022-10-09","ids":{"openalex":"https://openalex.org/W4309374805","doi":"https://doi.org/10.1109/smc53654.2022.9945189"},"language":"en","primary_location":{"id":"doi:10.1109/smc53654.2022.9945189","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc53654.2022.9945189","pdf_url":null,"source":{"id":"https://openalex.org/S4363607746","display_name":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5057935426","display_name":"Jing Bi","orcid":"https://orcid.org/0000-0002-4610-0141"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jing Bi","raw_affiliation_strings":["Beijing University of Technology,Faculty of Information Technology,Beijing,China,100124"],"affiliations":[{"raw_affiliation_string":"Beijing University of Technology,Faculty of Information Technology,Beijing,China,100124","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072916117","display_name":"Kangyuan Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kangyuan Xu","raw_affiliation_strings":["Beijing University of Technology,Faculty of Information Technology,Beijing,China,100124"],"affiliations":[{"raw_affiliation_string":"Beijing University of Technology,Faculty of Information Technology,Beijing,China,100124","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101881295","display_name":"Haitao Yuan","orcid":"https://orcid.org/0000-0001-8475-419X"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haitao Yuan","raw_affiliation_strings":["Beihang University,School of Automation Science and Electrical Engineering,Beijing,China,100191"],"affiliations":[{"raw_affiliation_string":"Beihang University,School of Automation Science and Electrical Engineering,Beijing,China,100191","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081318069","display_name":"MengChu Zhou","orcid":"https://orcid.org/0000-0002-5408-8752"},"institutions":[{"id":"https://openalex.org/I118118575","display_name":"New Jersey Institute of Technology","ror":"https://ror.org/05e74xb87","country_code":"US","type":"education","lineage":["https://openalex.org/I118118575"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"MengChu Zhou","raw_affiliation_strings":["New Jersey Institute of Technology,Department of Electrical and Computer Engineering,Newark,USA,07102"],"affiliations":[{"raw_affiliation_string":"New Jersey Institute of Technology,Department of Electrical and Computer Engineering,Newark,USA,07102","institution_ids":["https://openalex.org/I118118575"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5057935426"],"corresponding_institution_ids":["https://openalex.org/I37796252"],"apc_list":null,"apc_paid":null,"fwci":0.4324,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.49513348,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"15","issue":null,"first_page":"544","last_page":"549"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9993000030517578,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8441194295883179},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6551265716552734},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6476811170578003},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.56793612241745},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5300122499465942},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.526961624622345},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5086689591407776},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5007941722869873},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5001704692840576},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4960227906703949},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.47798246145248413},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.47290170192718506},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4689573645591736},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.4549810290336609},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.45391562581062317},{"id":"https://openalex.org/keywords/long-term-prediction","display_name":"Long-term prediction","score":0.44733038544654846},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33235886693000793},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.09811842441558838},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.07541146874427795}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8441194295883179},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6551265716552734},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6476811170578003},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.56793612241745},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5300122499465942},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.526961624622345},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5086689591407776},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5007941722869873},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5001704692840576},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4960227906703949},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.47798246145248413},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.47290170192718506},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4689573645591736},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.4549810290336609},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.45391562581062317},{"id":"https://openalex.org/C2776537626","wikidata":"https://www.wikidata.org/wiki/Q4047883","display_name":"Long-term prediction","level":2,"score":0.44733038544654846},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33235886693000793},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.09811842441558838},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.07541146874427795},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc53654.2022.9945189","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc53654.2022.9945189","pdf_url":null,"source":{"id":"https://openalex.org/S4363607746","display_name":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6499999761581421,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1906995411","https://openalex.org/W2061068374","https://openalex.org/W2095705004","https://openalex.org/W2109606373","https://openalex.org/W2166277028","https://openalex.org/W2548268629","https://openalex.org/W2586538484","https://openalex.org/W2605817989","https://openalex.org/W2752217602","https://openalex.org/W2767794223","https://openalex.org/W2787260237","https://openalex.org/W2792764867","https://openalex.org/W2956207189","https://openalex.org/W2981374454","https://openalex.org/W3042854933","https://openalex.org/W3097179687","https://openalex.org/W3109073683","https://openalex.org/W3165322078","https://openalex.org/W3213645633","https://openalex.org/W4385245566","https://openalex.org/W6631190155","https://openalex.org/W6674330103","https://openalex.org/W6684467336","https://openalex.org/W6732809304","https://openalex.org/W6749825310"],"related_works":["https://openalex.org/W2738221750","https://openalex.org/W2732542196","https://openalex.org/W3021430260","https://openalex.org/W2337926734","https://openalex.org/W3136076031","https://openalex.org/W4309045103","https://openalex.org/W2793022090","https://openalex.org/W4298168912","https://openalex.org/W3156786002","https://openalex.org/W4309374805"],"abstract_inverted_index":{"Precise":[0],"real-time":[1],"prediction":[2,81,100,172],"of":[3,6,87,101,171],"the":[4,67,85,99,115,126,152,162],"number":[5],"future":[7],"network":[8,22,102],"attacks":[9],"cannot":[10],"only":[11],"prompt":[12],"cloud":[13],"infrastructures":[14],"to":[15,18,52,111,121,134,145],"fast":[16],"respond":[17],"them":[19],"and":[20,28,39,44,96,148],"protect":[21],"security,":[23],"but":[24],"also":[25,63],"prevents":[26],"economic":[27],"business":[29],"losses.":[30],"In":[31,71],"recent":[32],"years,":[33],"neural":[34],"networks,":[35],"e.g.,":[36],"Bi-direction":[37],"Long":[38],"Short":[40],"Term":[41],"Memory":[42],"(LSTM)":[43],"Temporal":[45],"Convolutional":[46],"Network":[47],"(TCN),":[48],"have":[49],"been":[50],"proven":[51],"be":[53],"suitable":[54],"for":[55,66,98],"predicting":[56],"time":[57,68],"series":[58,69],"data.":[59,117],"Attention":[60],"mechanisms":[61],"are":[62],"widely":[64],"used":[65],"prediction.":[70],"this":[72,141],"work,":[73],"we":[74],"propose":[75],"a":[76,88,108,157],"novel":[77],"hybrid":[78],"deep":[79],"learning":[80],"method":[82,164],"by":[83],"combining":[84],"capabilities":[86],"Savitzky-Golay":[89],"(SG)":[90],"filter,":[91],"TCN,":[92],"Multi-head":[93],"self":[94,132],"attention,":[95],"BiLSTM":[97],"attacks.":[103],"This":[104],"work":[105,142],"first":[106],"adopts":[107,130,143],"SG":[109],"filter":[110],"eliminate":[112],"noise":[113],"in":[114,151,169],"raw":[116],"It":[118,128],"applies":[119],"TCN":[120],"extract":[122,146],"short-term":[123],"features":[124],"from":[125],"sequences.":[127,153],"then":[129],"multi-head":[131],"attention":[133],"capture":[135],"intrinsic":[136],"connections":[137],"among":[138],"features.":[139],"Finally,":[140],"Bi-LSTM":[144],"bi-directional":[147],"long-term":[149],"correlations":[150],"Experimental":[154],"results":[155],"with":[156],"real-life":[158],"dataset":[159],"show":[160],"that":[161],"proposed":[163],"outperforms":[165],"several":[166],"typical":[167],"algorithms":[168],"terms":[170],"accuracy.":[173]},"counts_by_year":[{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
