{"id":"https://openalex.org/W4404740404","doi":"https://doi.org/10.1109/icbase63199.2024.10762729","title":"Research on Penetration Attack Detection Based on CNN-BiGRU-Attention","display_name":"Research on Penetration Attack Detection Based on CNN-BiGRU-Attention","publication_year":2024,"publication_date":"2024-09-20","ids":{"openalex":"https://openalex.org/W4404740404","doi":"https://doi.org/10.1109/icbase63199.2024.10762729"},"language":"en","primary_location":{"id":"doi:10.1109/icbase63199.2024.10762729","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icbase63199.2024.10762729","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE)","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":null,"display_name":"Junyu Jia","orcid":null},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Junyu Jia","raw_affiliation_strings":["Beijing Information Science and Technology University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Information Science and Technology University,Beijing,China","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110076854","display_name":"Junling Ren","orcid":null},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junling Ren","raw_affiliation_strings":["Beijing Information Science and Technology University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Information Science and Technology University,Beijing,China","institution_ids":["https://openalex.org/I78675632"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I78675632"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.23842853,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"648","last_page":"654"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.8282999992370605,"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":0.8282999992370605,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/penetration","display_name":"Penetration (warfare)","score":0.6322876811027527},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6303712129592896},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46630918979644775},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.4380103647708893},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33345043659210205},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1310701072216034},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.09495875239372253}],"concepts":[{"id":"https://openalex.org/C80107235","wikidata":"https://www.wikidata.org/wiki/Q7162625","display_name":"Penetration (warfare)","level":2,"score":0.6322876811027527},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6303712129592896},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46630918979644775},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.4380103647708893},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33345043659210205},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1310701072216034},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.09495875239372253}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icbase63199.2024.10762729","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icbase63199.2024.10762729","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2147800946","https://openalex.org/W2575200128","https://openalex.org/W2591712613","https://openalex.org/W2889165715","https://openalex.org/W2954148997","https://openalex.org/W3017346274","https://openalex.org/W4214567394","https://openalex.org/W4229078496","https://openalex.org/W4311090320","https://openalex.org/W4386070139","https://openalex.org/W6775388223","https://openalex.org/W6788744375"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"With":[0],"the":[1,7,36,54,59,92,113,122,134,145,168],"rapid":[2],"development":[3],"of":[4,9,22,38,74,94,150,164,171,184],"network":[5,28],"technology,":[6],"issue":[8],"cybersecurity":[10],"has":[11],"become":[12],"increasingly":[13],"severe.":[14],"Network":[15,104],"penetration":[16,42,50,75,86,114,152,172],"attacks,":[17],"as":[18],"an":[19,97,162],"advanced":[20],"form":[21],"cyberattacks,":[23],"pose":[24],"significant":[25],"harm":[26],"to":[27,78,108,132],"systems.":[29],"Current":[30],"research":[31],"focuses":[32],"relatively":[33],"little":[34],"on":[35,58,91],"detection":[37,88,170],"malicious":[39,55,80,115],"traffic":[40,56,116],"from":[41,112],"attacks.":[43,185],"This":[44],"paper":[45,83],"thoroughly":[46],"studies":[47],"and":[48,62,70,141,182],"analyzes":[49],"attack":[51,72,87,153,173],"behaviors,":[52,174],"classifying":[53],"based":[57,90],"ATT&CK":[60],"framework":[61],"reconstructing":[63],"a":[64,85,101,124],"dataset":[65],"that":[66,158],"covers":[67],"all":[68],"stages":[69,181],"various":[71],"techniques":[73],"attacks.In":[76],"response":[77],"this":[79,82,159],"traffic,":[81],"proposes":[84],"method":[89,160],"combination":[93],"CNN-BiGRU":[95],"with":[96],"attention":[98],"mechanism.":[99],"First,":[100],"Convolutional":[102],"Neural":[103],"(CNN)":[105],"is":[106,130],"used":[107,131],"extract":[109],"spatial":[110],"features":[111],"data,":[117,146],"capturing":[118,138],"local":[119],"information":[120,140],"in":[121,167,178],"data.Then,":[123],"Bidirectional":[125],"Gated":[126],"Recurrent":[127],"Unit":[128],"(BiGRU)":[129],"model":[133],"extracted":[135],"feature":[136],"sequences,":[137],"temporal":[139],"long-term":[142],"dependencies":[143],"within":[144],"enabling":[147],"precise":[148],"identification":[149],"complex":[151],"behaviors.":[154],"Experimental":[155],"results":[156],"show":[157],"achieves":[161],"accuracy":[163],"$96.28":[165],"\\%$":[166],"multi-class":[169],"demonstrating":[175],"its":[176],"effectiveness":[177],"handling":[179],"different":[180],"strategies":[183]},"counts_by_year":[],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2025-10-10T00:00:00"}