{"id":"https://openalex.org/W4392078014","doi":"https://doi.org/10.1145/3640912.3640929","title":"Application Identification Based on Overlap Relationship of Concurrent Flows and Their Durations","display_name":"Application Identification Based on Overlap Relationship of Concurrent Flows and Their Durations","publication_year":2023,"publication_date":"2023-10-27","ids":{"openalex":"https://openalex.org/W4392078014","doi":"https://doi.org/10.1145/3640912.3640929"},"language":"en","primary_location":{"id":"doi:10.1145/3640912.3640929","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3640912.3640929","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 International Conference on Communication Network and Machine 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/A5006098940","display_name":"Jinhua Lu","orcid":"https://orcid.org/0009-0003-9145-6766"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinhua Lu","raw_affiliation_strings":["School of Computer Science and Engineering, Sun Yat-sen University, China"],"raw_orcid":"https://orcid.org/0009-0003-9145-6766","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Sun Yat-sen University, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100768971","display_name":"Shun\u2010Zheng Yu","orcid":"https://orcid.org/0000-0002-7797-9224"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shunzheng Yu","raw_affiliation_strings":["School of Computer Science and Engineering, Sun Yat-sen University, China"],"raw_orcid":"https://orcid.org/0000-0002-7797-9224","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Sun Yat-sen University, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1897,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.55546332,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"88","last_page":"92"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9995999932289124,"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":0.9995999932289124,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.9994999766349792,"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/identification","display_name":"Identification (biology)","score":0.7383473515510559},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6070600152015686},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.08440086245536804}],"concepts":[{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.7383473515510559},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6070600152015686},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.08440086245536804},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3640912.3640929","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3640912.3640929","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 International Conference on Communication Network and Machine Learning","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1993520820","https://openalex.org/W2127939887","https://openalex.org/W2190207511","https://openalex.org/W2269460187","https://openalex.org/W2560100125","https://openalex.org/W2750674396","https://openalex.org/W2782688857","https://openalex.org/W2783320270","https://openalex.org/W2919493784","https://openalex.org/W2949330846","https://openalex.org/W2978116268","https://openalex.org/W3120227884","https://openalex.org/W4293004633","https://openalex.org/W6693823607"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W4395014643","https://openalex.org/W4391913857","https://openalex.org/W2350741829"],"abstract_inverted_index":{"The":[0,50,142],"paper":[1,55],"introduces":[2],"a":[3,31,73,89,148],"novel":[4],"approach":[5,51,113],"for":[6],"application":[7,140],"identification":[8],"in":[9,53,88,166],"the":[10,76,101,116,123,137,156],"field":[11],"of":[12,118,125,139,168],"network":[13],"security,":[14],"where":[15],"numerous":[16],"research":[17],"efforts":[18],"have":[19,30],"been":[20],"dedicated,":[21],"leading":[22],"to":[23],"noteworthy":[24],"accomplishments.":[25],"However,":[26],"most":[27],"prior":[28],"studies":[29],"limitation":[32],"since":[33],"they":[34],"primarily":[35],"focus":[36],"on":[37,44,65,83,147],"single":[38],"flows,":[39,133],"thus":[40],"lacking":[41],"comprehensive":[42],"discussions":[43],"multiple":[45],"flows":[46,58,79],"and":[47,59,62,75,106,128,155,171],"their":[48,66,84],"relationships.":[49,67],"proposed":[52,143],"this":[54],"utilizes":[56],"concurrent":[57],"constructs":[60],"overlap":[61,85],"duration-based":[63],"graphs":[64],"Each":[68],"flow":[69,120],"is":[70,80,145],"represented":[71],"as":[72],"node,":[74],"connectivity":[77],"between":[78],"established":[81],"based":[82],"relationships,":[86],"resulting":[87],"graph":[90,103],"representation":[91],"that":[92],"captures":[93],"these":[94],"connections.":[95],"To":[96],"extract":[97],"meaningful":[98],"features":[99],"from":[100],"graph,":[102],"attention":[104],"mechanisms":[105],"convolutional":[107],"neural":[108],"networks":[109],"are":[110],"employed.":[111],"This":[112],"effectively":[114],"highlights":[115],"significance":[117],"longer":[119],"durations,":[121],"enabling":[122],"expression":[124],"temporal":[126],"sequencing":[127],"potential":[129],"triggering":[130],"relationships":[131],"among":[132],"thereby":[134],"significantly":[135],"improving":[136],"accuracy":[138],"identification.":[141],"method":[144],"evaluated":[146],"real-world":[149],"dataset":[150],"comprising":[151],"17":[152],"diverse":[153],"applications,":[154],"experimental":[157],"results":[158],"demonstrate":[159],"its":[160],"superiority":[161],"over":[162],"other":[163],"existing":[164],"methods":[165],"terms":[167],"accuracy,":[169],"precision,":[170],"F1-Score.":[172]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}