{"id":"https://openalex.org/W7140236413","doi":"https://doi.org/10.1504/ijbidm.2026.152484","title":"Attention and deep feature-based intelligent approach for abnormal network traffic detection","display_name":"Attention and deep feature-based intelligent approach for abnormal network traffic detection","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7140236413","doi":"https://doi.org/10.1504/ijbidm.2026.152484"},"language":"en","primary_location":{"id":"doi:10.1504/ijbidm.2026.152484","is_oa":false,"landing_page_url":"https://doi.org/10.1504/ijbidm.2026.152484","pdf_url":null,"source":{"id":"https://openalex.org/S47982532","display_name":"International Journal of Business Intelligence and Data Mining","issn_l":"1743-8187","issn":["1743-8187","1743-8195"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310317825","host_organization_name":"Inderscience Publishers","host_organization_lineage":["https://openalex.org/P4310317825"],"host_organization_lineage_names":["Inderscience Publishers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Business Intelligence and Data Mining","raw_type":"journal-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":null,"display_name":"Guihua Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Guihua Wu","raw_affiliation_strings":["Information School, Changde College, Changde, Hunan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information School, Changde College, Changde, Hunan, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.65980342,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"28","issue":"2/3","first_page":"230","last_page":"242"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.4578000009059906,"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":0.4578000009059906,"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":0.17110000550746918,"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":0.06759999692440033,"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/anomaly-detection","display_name":"Anomaly detection","score":0.6757000088691711},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.531000018119812},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.477400004863739},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.4702000021934509},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.4657999873161316},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4657000005245209},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.4399000108242035},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.39969998598098755}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8878999948501587},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6757000088691711},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6140999794006348},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.531499981880188},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.531000018119812},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.477400004863739},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.4702000021934509},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.4657999873161316},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4657000005245209},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4399000108242035},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42309999465942383},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.39969998598098755},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3840999901294708},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.37700000405311584},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.35569998621940613},{"id":"https://openalex.org/C39927690","wikidata":"https://www.wikidata.org/wiki/Q11197","display_name":"Logarithm","level":2,"score":0.31209999322891235},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2962000072002411},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.27480000257492065},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2565000057220459}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1504/ijbidm.2026.152484","is_oa":false,"landing_page_url":"https://doi.org/10.1504/ijbidm.2026.152484","pdf_url":null,"source":{"id":"https://openalex.org/S47982532","display_name":"International Journal of Business Intelligence and Data Mining","issn_l":"1743-8187","issn":["1743-8187","1743-8195"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310317825","host_organization_name":"Inderscience Publishers","host_organization_lineage":["https://openalex.org/P4310317825"],"host_organization_lineage_names":["Inderscience Publishers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Business Intelligence and Data Mining","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Network":[0],"traffic":[1,86],"anomaly":[2,65],"detection":[3,66,126,132],"faces":[4],"critical":[5],"challenges":[6],"in":[7],"feature":[8,52,78],"extraction":[9,79],"robustness,":[10],"and":[11,19,37,58,92,103,129],"computational":[12,39],"efficiency":[13],"due":[14],"to":[15,62],"increasing":[16],"data":[17,111],"dimensionality":[18],"environmental":[20],"noise.":[21],"Existing":[22],"deep":[23,51],"learning":[24],"approaches":[25],"offer":[26],"partial":[27],"improvements":[28],"but":[29],"suffer":[30],"from":[31],"noise":[32],"sensitivity,":[33],"structural":[34,85],"information":[35],"neglect,":[36],"unnecessary":[38],"overhead.":[40],"This":[41],"paper":[42],"presents":[43],"an":[44,90],"intelligent":[45],"approach":[46,119],"integrating":[47],"attention":[48,56],"mechanisms":[49,106],"with":[50,89],"learning,":[53],"combining":[54],"multi-scale":[55],"dynamics":[57],"optimised":[59,93],"gradient":[60,94],"boosting":[61],"address":[63],"network":[64],"challenges.":[67],"The":[68],"core":[69],"contributions":[70],"encompass":[71],"a":[72],"hybrid":[73],"solution":[74],"that":[75,116],"achieves":[76],"noise-resilient":[77],"through":[80],"self-attention":[81],"weighting":[82],"while":[83],"preserving":[84],"patterns,":[87],"coupled":[88],"enhanced":[91],"boosted":[95],"decision":[96],"tree":[97],"classifier":[98],"employing":[99],"logarithmic":[100],"loss":[101],"optimisation":[102],"early":[104],"stopping":[105],"for":[107],"effective":[108],"high-dimensional":[109],"sparse":[110],"processing.":[112],"Experimental":[113],"results":[114],"demonstrate":[115],"the":[117,121],"proposed":[118],"outperforms":[120],"state-of-the-art":[122],"baselines,":[123],"including":[124],"superior":[125],"accuracy":[127],"98.2%":[128],"around":[130],"34.7%":[131],"time":[133],"reduction.":[134]},"counts_by_year":[],"updated_date":"2026-04-25T08:17:42.794288","created_date":"2026-03-25T00:00:00"}
