{"id":"https://openalex.org/W4399257589","doi":"https://doi.org/10.1145/3653781.3653807","title":"A CNN-BiLSTM Method Based on Attention Mechanism for Class-imbalanced Abnormal Traffic Detection","display_name":"A CNN-BiLSTM Method Based on Attention Mechanism for Class-imbalanced Abnormal Traffic Detection","publication_year":2024,"publication_date":"2024-01-19","ids":{"openalex":"https://openalex.org/W4399257589","doi":"https://doi.org/10.1145/3653781.3653807"},"language":"en","primary_location":{"id":"doi:10.1145/3653781.3653807","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3653781.3653807","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Computer Vision and Deep Learning","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/A5098972481","display_name":"Jiali Yin","orcid":"https://orcid.org/0009-0001-4762-1002"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiali Yin","raw_affiliation_strings":["School of Electronic Engineering Beijing University of Posts and Telecommunications, China"],"raw_orcid":"https://orcid.org/0009-0001-4762-1002","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101506141","display_name":"Bin Hou","orcid":"https://orcid.org/0000-0003-2100-0520"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Hou","raw_affiliation_strings":["School of Electronic Engineering Beijing University of Posts and Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0003-2100-0520","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013853512","display_name":"Jiapeng Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiapeng Dai","raw_affiliation_strings":["School of Electronic Engineering Beijing University of Posts and Telecommunications, China"],"raw_orcid":"https://orcid.org/0009-0005-0383-3715","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007639953","display_name":"Yunxiao Zu","orcid":"https://orcid.org/0000-0002-9227-1574"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunxiao Zu","raw_affiliation_strings":["School of Electronic Engineering Beijing University of Posts and Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0002-9227-1574","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5098972481"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.6728,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.69202375,"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":"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":0.9995999932289124,"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":0.998199999332428,"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":0.8039391040802002},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.711036205291748},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6522671580314636},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5477726459503174},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5238835215568542},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49527421593666077},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3658592700958252}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8039391040802002},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.711036205291748},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6522671580314636},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5477726459503174},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5238835215568542},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49527421593666077},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3658592700958252}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3653781.3653807","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3653781.3653807","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Computer Vision and Deep Learning","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":8,"referenced_works":["https://openalex.org/W433644524","https://openalex.org/W2296509296","https://openalex.org/W4206141920","https://openalex.org/W4285732709","https://openalex.org/W4313654696","https://openalex.org/W4385245566","https://openalex.org/W4386590181","https://openalex.org/W4387191620"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W2357468538","https://openalex.org/W1577110157","https://openalex.org/W2355007334","https://openalex.org/W2390009783","https://openalex.org/W4254602698","https://openalex.org/W2364419519","https://openalex.org/W2360767377","https://openalex.org/W2017948608","https://openalex.org/W2360951146"],"abstract_inverted_index":{"Abstract:":[0],"Network":[1],"traffic":[2,25],"anomaly":[3,130,209],"detection":[4,34,39,45,62,85,160,196],"technology":[5],"is":[6],"an":[7,72,103],"effective":[8],"method":[9],"for":[10],"monitoring":[11],"network":[12,24,80,129],"security":[13],"status":[14],"and":[15,30,41,56,90,101,120,165],"identifying":[16],"potential":[17],"attacks.":[18],"With":[19],"the":[20,99,135,151,159,167,174,179,195],"increasing":[21],"volume":[22],"of":[23,98,162,186,198],"data":[26,55],"exhibiting":[27],"high":[28,184],"dimensionality":[29],"imbalance,":[31],"traditional":[32],"intrusion":[33,44,61,84],"models":[35],"face":[36],"limitations":[37],"in":[38,54,128,204],"efficiency":[40],"accuracy.":[42],"Existing":[43],"methods":[46],"often":[47],"overlook":[48],"issues":[49,127],"related":[50],"to":[51,66,82,93,106,123],"temporal":[52],"features":[53,97],"class":[57,125,206],"imbalances,":[58],"further":[59],"hindering":[60],"efficiency.":[63,86,121,190],"In":[64],"response":[65],"these":[67],"challenges,":[68],"this":[69,112,132],"paper":[70],"proposes":[71],"attention-based":[73],"convolutional":[74],"neural":[75],"networks\u2013long":[76],"short-term":[77],"memory":[78],"(CNN-BiLSTM)":[79],"structure":[81],"enhance":[83],"By":[87],"integrating":[88],"CNN":[89],"BiLSTM":[91],"networks":[92],"jointly":[94],"learn":[95],"spatio-temporal":[96],"data,":[100],"introducing":[102,140],"attention":[104],"mechanism":[105],"deeply":[107],"capture":[108],"hidden":[109],"feature":[110],"relationships,":[111],"approach":[113],"accelerates":[114],"model":[115],"convergence":[116],"while":[117,188],"balancing":[118],"accuracy":[119,185],"Additionally,":[122],"address":[124],"imbalance":[126],"traffic,":[131],"study":[133],"optimizes":[134],"cross-entropy":[136],"loss":[137,144],"function":[138,145,148],"by":[139],"a":[141,183],"cyclical":[142],"focal":[143],"(CFL).":[146],"This":[147],"cyclically":[149],"adjusts":[150],"model's":[152,168],"focus":[153],"on":[154,173],"different":[155],"samples,":[156,200],"thereby":[157],"enhancing":[158],"rate":[161,197],"challenging":[163],"samples":[164],"improving":[166],"generalization":[169],"capabilities.":[170],"Experiments":[171],"conducted":[172],"UNSW-NB15":[175],"datasets":[176],"demonstrate":[177],"that":[178],"proposed":[180],"algorithm":[181],"achieves":[182],"97.09%":[187],"ensuring":[189],"Moreover,":[191],"it":[192],"effectively":[193],"enhances":[194],"imbalanced":[199],"highlighting":[201],"its":[202],"capability":[203],"handling":[205],"imbalances":[207],"within":[208],"traffic.":[210]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
