{"id":"https://openalex.org/W3011139431","doi":"https://doi.org/10.3390/e22030324","title":"An Efficient Alert Aggregation Method Based on Conditional Rough Entropy and Knowledge Granularity","display_name":"An Efficient Alert Aggregation Method Based on Conditional Rough Entropy and Knowledge Granularity","publication_year":2020,"publication_date":"2020-03-12","ids":{"openalex":"https://openalex.org/W3011139431","doi":"https://doi.org/10.3390/e22030324","mag":"3011139431","pmid":"https://pubmed.ncbi.nlm.nih.gov/33286098"},"language":"en","primary_location":{"id":"doi:10.3390/e22030324","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e22030324","pdf_url":"https://www.mdpi.com/1099-4300/22/3/324/pdf?version=1584530123","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/22/3/324/pdf?version=1584530123","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5089952792","display_name":"Jiaxuan Sun","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":"Jiaxuan Sun","raw_affiliation_strings":["Institute of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101553858","display_name":"Lize Gu","orcid":"https://orcid.org/0000-0002-2534-7532"},"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":"Lize Gu","raw_affiliation_strings":["Institute of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101607322","display_name":"Kaiyuan Chen","orcid":"https://orcid.org/0000-0002-8847-4870"},"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":"Kaiyuan Chen","raw_affiliation_strings":["Institute of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101553858"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2227},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2227},"fwci":1.8767,"has_fulltext":true,"cited_by_count":18,"citation_normalized_percentile":{"value":0.88353587,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"22","issue":"3","first_page":"324","last_page":"324"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9991999864578247,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9991999864578247,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9986000061035156,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/granularity","display_name":"Granularity","score":0.8879698514938354},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.7904582619667053},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.7852975130081177},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7715998888015747},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.6791456341743469},{"id":"https://openalex.org/keywords/rough-set","display_name":"Rough set","score":0.6366538405418396},{"id":"https://openalex.org/keywords/conditional-entropy","display_name":"Conditional entropy","score":0.6169360280036926},{"id":"https://openalex.org/keywords/granular-computing","display_name":"Granular computing","score":0.44365715980529785},{"id":"https://openalex.org/keywords/similarity-measure","display_name":"Similarity measure","score":0.4170433282852173},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.25033873319625854},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.19924849271774292}],"concepts":[{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.8879698514938354},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.7904582619667053},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.7852975130081177},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7715998888015747},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.6791456341743469},{"id":"https://openalex.org/C111012933","wikidata":"https://www.wikidata.org/wiki/Q3137210","display_name":"Rough set","level":2,"score":0.6366538405418396},{"id":"https://openalex.org/C101721835","wikidata":"https://www.wikidata.org/wiki/Q813908","display_name":"Conditional entropy","level":3,"score":0.6169360280036926},{"id":"https://openalex.org/C17209119","wikidata":"https://www.wikidata.org/wiki/Q5596712","display_name":"Granular computing","level":3,"score":0.44365715980529785},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.4170433282852173},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25033873319625854},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.19924849271774292},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"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}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/e22030324","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e22030324","pdf_url":"https://www.mdpi.com/1099-4300/22/3/324/pdf?version=1584530123","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmid:33286098","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33286098","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:735a7f2a3d084561af4193ef43a307f4","is_oa":true,"landing_page_url":"https://doaj.org/article/735a7f2a3d084561af4193ef43a307f4","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 22, Iss 3, p 324 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1099-4300/22/3/324/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/e22030324","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:7516779","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7516779","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/e22030324","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e22030324","pdf_url":"https://www.mdpi.com/1099-4300/22/3/324/pdf?version=1584530123","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8424202872","display_name":null,"funder_award_id":"No. 2017YFB080301","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3011139431.pdf","grobid_xml":"https://content.openalex.org/works/W3011139431.grobid-xml"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W577421504","https://openalex.org/W1488532897","https://openalex.org/W1582506709","https://openalex.org/W1987553702","https://openalex.org/W1990902999","https://openalex.org/W1995875735","https://openalex.org/W2000158142","https://openalex.org/W2009653394","https://openalex.org/W2035567190","https://openalex.org/W2058740153","https://openalex.org/W2077454161","https://openalex.org/W2092179332","https://openalex.org/W2095979141","https://openalex.org/W2108867737","https://openalex.org/W2114383673","https://openalex.org/W2115149820","https://openalex.org/W2117632631","https://openalex.org/W2122646361","https://openalex.org/W2128064123","https://openalex.org/W2132625653","https://openalex.org/W2142864059","https://openalex.org/W2161830378","https://openalex.org/W2163277533","https://openalex.org/W2165094119","https://openalex.org/W2183658013","https://openalex.org/W2318901577","https://openalex.org/W2412250047","https://openalex.org/W2518081282","https://openalex.org/W2594016977","https://openalex.org/W2612102978","https://openalex.org/W2725271592","https://openalex.org/W2784005036","https://openalex.org/W2789828921","https://openalex.org/W2795075588","https://openalex.org/W2829082624","https://openalex.org/W2900713154","https://openalex.org/W2944855837","https://openalex.org/W2954221862","https://openalex.org/W2963832721","https://openalex.org/W2979704987","https://openalex.org/W2982278495","https://openalex.org/W2990352665","https://openalex.org/W2997677709","https://openalex.org/W3008066310","https://openalex.org/W6633188799","https://openalex.org/W6683581553","https://openalex.org/W6686206910","https://openalex.org/W7074150349"],"related_works":["https://openalex.org/W1795409297","https://openalex.org/W2508737459","https://openalex.org/W2734666672","https://openalex.org/W2385082087","https://openalex.org/W2013421631","https://openalex.org/W2390772058","https://openalex.org/W2282249876","https://openalex.org/W2978631811","https://openalex.org/W2367964367","https://openalex.org/W2374419214"],"abstract_inverted_index":{"With":[0],"the":[1,41,64,71,86,96,102,111,126,130,136,141,155,160,172],"emergence":[2],"of":[3,15,43,81,89,98,162,175],"network":[4,50],"security":[5,8,51],"issues,":[6],"various":[7],"devices":[9],"that":[10,29,149],"generate":[11],"a":[12,119],"large":[13],"number":[14],"logs":[16],"and":[17,36,45,59,75,140,158,168,178],"alerts":[18,91,112,157],"are":[19],"widely":[20],"used.":[21],"This":[22,67],"paper":[23],"proposes":[24],"an":[25],"alert":[26,47,176],"aggregation":[27],"scheme":[28,132],"is":[30,107,116,122,133],"based":[31,94],"on":[32,95],"conditional":[33,56],"rough":[34,57],"entropy":[35,58],"knowledge":[37,60],"granularity":[38,61],"to":[39,62,109,124,135],"solve":[40],"problem":[42],"repetitive":[44],"redundant":[46,127,156],"information":[48],"in":[49],"devices.":[52],"Firstly,":[53],"we":[54],"use":[55],"determine":[63,70],"attribute":[65,99],"weights.":[66],"method":[68,106,151],"can":[69,84,152],"different":[72,79],"important":[73],"attributes":[74],"their":[76],"weights":[77],"for":[78,171],"types":[80],"attacks.":[82],"We":[83],"calculate":[85],"similarity":[87,114],"value":[88,115],"two":[90],"by":[92],"weighting":[93],"results":[97,147],"weighting.":[100],"Subsequently,":[101],"sliding":[103],"time":[104],"window":[105],"used":[108],"aggregate":[110],"whose":[113],"larger":[117],"than":[118],"threshold,":[120],"which":[121],"set":[123],"reduce":[125,154],"alerts.":[128],"Finally,":[129],"proposed":[131],"applied":[134],"CIC-IDS":[137],"2018":[138],"dataset":[139],"DARPA":[142],"98":[143],"dataset.":[144],"The":[145],"experimental":[146],"show":[148],"this":[150],"effectively":[153],"improve":[159],"efficiency":[161],"data":[163,170],"processing,":[164],"thus":[165],"providing":[166],"accurate":[167],"concise":[169],"next":[173],"stage":[174],"fusion":[177],"analysis.":[179]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":5}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}