{“id”:“https://openalex.org/W4200128868网址“,”doi“:”https://doi.org/10.3233/jifs-201707“,”title“:“使用Deep Auto-Encoder和优化的基于LSTM的Deep Learning方法检测云日志中的异常的混合方法”,“display_name”:“使用Deep Auto-Encoder的混合方法和基于优化的LSTM的深度学习方法检测云记录中的异常”,“publication_year”:2021,“publitation_date”:“2021-12-07”,“ids”:{“openalex”:“https://openalex.org/W4200128868网址“,”doi“:”https://doi.org/10.3233/jifs-201707“},”language“:”en“,”primary_location“:{”is_oa“:false,”landing_page_url“:”https://doi.org/10.3233/jifs-201707“,”pdf_url“:空,”源“:{”id“:”https://openalex.org/S179157397“,”display_name“:”智能与模糊系统杂志“,”issn_l“:”1064-1246“,”isn“:[”1064-1146“,”1875-8967“],”is_oa“:false,”is_in_doaj“:false,”is_core“:true,”host_organization“:”https://openalex.org/P4310318577“,”“host_organization_name”:“IOS新闻”,“host_organization_lineage”:[“https://openalex.org/P4310318577“],”host_organization_lineage_names“:[”IOS Press“],”type“:”journal“},”license“:null,”license_id“:null,”version“:null,”is_accepted“:false,”is_published“:false},”type“:”article“,”type_crossref“:”journal 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/A5068866446“,”display_name“:”Savaridassan Pankajashan“,”orcid“:null},”institutions“:[{”id“:”https://openalex.org/I145286018“,”display_name“:”SRM科技学院“,”ror“:”https://ror.org/050113w36“,”country_code“:”IN“,”type“:”教育“,”血统“:[”https://openalex.org/I145286018“]}],”国家“:[”IN“],”is_corresponding“:true,”raw_author_name“:”Savaridassan Pankajashan“,”raw _affiliation_strings“:[“印度塔米纳德邦钦奈市卡坦库亚拉图尔SRM科学技术研究所信息技术部”],“affiliations”:[{“raw_affilition_string”:“印度塔米纳德邦钦奈Kattankualathur SRM科学技术研究所信息技术部“,“institution_ids”:[“https://openalex.org/I145286018“]}]},{”author_position“:”middle“,”author“:{”id“:”https://openalex.org/A5038588806“,”display_name“:”G.Maragatham“,”orcid“:null},”institutions“:[{”id“:”https://openalex.org/I145286018“,”display_name“:”SRM科技学院“,”ror“:”https://ror.org/050113w36“,”country_code“:”IN“,”type“:”教育“,”血统“:[”https://openalex.org/I145286018“]}],”国家“:[”IN“],”is_corresponding“:false,”raw_author_name“:”G.Maragatham“,”raw _affiliation_strings“:[“印度塔米纳德邦钦奈,卡坦库阿拉图尔,SRM科学技术研究所信息技术部”],“affiliations”:[{“raw_affilition_string”:“印度塔米纳德邦钦奈Kattankualathur SRM科学技术研究所信息技术系“,”机构ID“:[”https://openalex.org/I145286018“]}]},{”author_position“:”last“,”author“:{”id“:”https://openalex.org/A5035735404“,”display_name“:”T.Kirthiga Devi“,”orcid“:null},”institutions“:[{”id“:”https://openalex.org/I145286018“,”display_name“:”SRM科技学院“,”ror“:”https://ror.org/050113w36“,”country_code“:”IN“,”type“:”教育“,”血统“:[”https://openalex.org/I145286018“]}],”国家“:[”IN“],”is_corresponding“:false,”raw_author_name“:”T.Kirthiga Devi“,”raw _affiliation_strings“:[“印度塔米纳德邦钦奈SRM科学技术研究所信息技术部”],“affiliations”:[{“raw_affiliation _string”:“印度塔米纳德邦钦奈Kattankualathur SRM科学技术研究所信息技术部“,“institution_ids”:[“https://openalex.org/I145286018“]}]}],”institution_assertions“:[],”countries_distiction_count“:1,”institutions_disticent_count”:1,“corresponding_author_ids”:[“https://openalex.org/A5068866446“],”对应的机构ID“:[”https://openalex.org/I145286018“],”apc_list“:null,”apc _ paid“:nul,”fwci“:0.0,”has_fulltext“:false,”cited_by_count“:0,”引用规范化百分比“:{”value“:0.05,”is_in_top_1_percent“:false,”is_ in_top_ 10_percennt“:false},”citected_by_percentile_year“:{min”:0,“max”:58},“biblio”:{“volume”:“42”,“issue”:“6”,“first_page”:”6257“,”last_page“:”6271“},”is_retracted“:false,”is_paratext“:false,”primary_topic“:{”id“:”https://openalex.org/T10400“,”“display_name”:“网络入侵检测和防御机制”,“score”:0.9999,“subfield”:{“id”:“https://openalex.org/subfields/1705“,”display_name“:”计算机网络和通信“},”字段“:{”id“:”https://openalex.org/fields/17“,”display_name“:”Computer Science“},”domain“:{”id“:”https://openalex.org/domains/3“,”display_name“:”物理科学“}},”主题“:[{”id“:”https://openalex.org/T10400“,”“display_name”:“网络入侵检测和防御机制”,“score”:0.9999,“subfield”:{“id”:“https://openalex.org/subfields/1705“,”display_name“:”计算机网络和通信“},”字段“:{”id“:”https://openalex.org/fields/17“,”display_name“:”Computer Science“},”domain“:{”id“:”https://openalex.org/domains/3“,”display_name“:”物理科学“}},{”id“:”https://openalex.org/T12127“,”display_name“:”日志分析和系统性能诊断“,”score“:0.9998,”subfield“:{”id“:”https://openalex.org/subfields/1705“,”display_name“:”计算机网络和通信“},”字段“:{”id“:”https://openalex.org/fields/17“,”display_name“:”Computer Science“},”domain“:{”id“:”https://openalex.org/domains/3“,”display_name“:”物理科学“}},{”id“:”https://openalex.org/T11512“,”display_name“:”高维数据中的异常检测“,”score“:0.9998,”subfield“:{”id“:”https://openalex.org/subfields/1702“,”display_name“:”人工智能“},”字段“:{”id“:”https://openalex.org/fields/17“,”display_name“:”Computer Science“},”domain“:{”id“:”https://openalex.org/domains/3“,”display_name“:”物理科学“}}],”关键词“:[{”id“:”https://openalex.org/keywords/anomaly-detection网站“,”display_name“:”异常检测“,”score“:0.636181},{”id“:”https://openalex.org/keywords/outlier-detection“,”display_name“:”离群检测“,”score“:0.598292},{”id“:”https://openalex.org/keywords/autoencoder“,”display_name“:”自动编码器“,”score“:0.58805424},{”id“:”https://openalex.org/keywords/botnet检测“,”display_name“:”僵尸网络检测“,”score“:0.541318},{”id“:”https://openalex.org/keywords/intrusion-detection网站“,”display_name“:”入侵检测“,”score“:0.539402},{”id“:”https://openalex.org/keywords/log-analysis网站“,”display_name“:”Log Analysis“,”score“:0.532327},{”id“:”https://openalex.org/keywords/anormation(https://openalex.org/关键词/异常)“,”display_name“:”异常(物理)“,”score“:0.4484949}],”concepts“:[{”id“:”https://openalex.org/C41008148,“wikidata”:https://www.wikidata.org/wiki/Q21198“,”display_name“:”计算机科学“,”level“:0,”score“:0.8213081},{”id“:”https://openalex.org/C739882,“wikidata”:https://www.wikidata.org/wiki/Q3560506“,”display_name“:”异常检测“,”level“:2,”score“:0.76000553},{”id“:”https://openalex.org/C154945302,“wikidata”:https://www.wikidata.org/wiki/Q11660“,”display_name“:”人工智能“,”level“:1,”score“:0.69135535},{”id“:”https://openalex.org/C108583219,“wikidata”:https://www.wikidata.org/wiki/Q197536“,”display_name“:”深度学习“,”level“:2,”score“:0.68695503},{”id“:”https://openalex.org/C101738243,“wikidata”:https://www.wikidata.org/wiki/Q786435“,”display_name“:”自动编码器“,”level“:3,”score“:0.58805424},{”id“:”https://openalex.org/C50644808,“wikidata”:https://www.wikidata.org/wiki/Q192776“,”display_name“:”人工神经网络“,”level“:2,”score“:0.53645426},{”id“:”https://openalex.org/C79974875,“wikidata”:https://www.wikidata.org/wiki/Q483639“,”display_name“:”云计算“,”level“:2,”score“:0.48996106},{”id“:”https://openalex.org/C12997251,“wikidata”:https://www.wikidata.org/wiki/Q567560“,”display_name“:”异常(物理)“,”level“:2,”score“:0.4484949},{”id“:”https://openalex.org/C119857082,“wikidata”:https://www.wikidata.org/wiki/Q2539“,”display_name“:”机器学习“,”level“:1,”score“:0.44661847},{”id“:”https://openalex.org/C147168706,“wikidata”:https://www.wikidata.org/wiki/Q1457734“,”display_name“:”递归神经网络“,”level“:3,”score“:0.44189125},{”id“:”https://openalex.org/C124101348,“wikidata”:https://www.wikidata.org/wiki/Q172491网址“,”display_name“:”数据挖掘“,”level“:1,”score“:0.3502076},{”id“:”https://openalex.org/C121332964,“wikidata”:https://www.wikidata.org/wiki/Q413“,”display_name“:”物理“,”级别“:0,”分数“:0.0},{”id“:”https://openalex.org/C26873012,“wikidata”:https://www.wikidata.org/wiki/Q214781“,”display_name“:”凝聚态物理学“,”level“:1,”score“:0.0},{”id“:”https://openalex.org/C111919701,“wikidata”:https://www.wikidata.org/wiki/Q9135“,”display_name“:”操作系统“,”level“:1,”score“:0.0}],”mesh“:[],”locations_count“:1.”locations“:[{”is_oa“:false,”landing_page_url“:”https://doi.org/10.3233/jifs-201707“,”pdf_url“:空,”源“:{”id“:”https://openalex.org/S179157397“,”display_name“:”智能与模糊系统杂志“,”issn_l“:”1064-1246“,”isn“:[”1064-1146“,”1875-8967“],”is_oa“:false,”is_in_doaj“:false,”is_core“:true,”host_organization“:”https://openalex.org/P4310318577“,”“host_organization_name”:“IOS新闻”,“host_organization_lineage”:[“https://openalex.org/P4310318577“],”host_organization_lineage_names“:[”IOS Press“],“type”:“journal”},“license”:null,“licence_id”:null,“version”:nul,“is_accepted”:false,“is_published”:false}],“best_oa_location”:null,“sustainable_development_goals”:[{“display_name”:“Life on land”,“score”:0.55,“id”:“https://metadata.un.org/sdg/15“}],”grants“:[],”datasets“:[],”versions“:[】,”referenced_works_count“:26,”referrenced_works“:【”https://openalex.org/W2066877142","https://openalex.org/W2296719434","https://openalex.org/W2335999708","https://openalex.org/W2340896621","https://openalex.org/W2762776925","https://openalex.org/W2789295346","https://openalex.org/W2794048982","https://openalex.org/W2801080689","https://openalex.org/W2803881474","https://openalex.org/W2886020981","https://openalex.org/W2898998129","https://openalex.org/W2899599931","https://openalex.org/W2914715487","https://openalex.org/W2921900206","https://openalex.org/W2926701059","https://openalex.org/W2942788712","https://openalex.org/W2944555271","https://openalex.org/W2944992190","https://openalex.org/W2945915315","https://openalex.org/W2950883759","https://openalex.org/W2956545777","https://openalex.org/W2963999143","https://openalex.org/W2980576170","https://openalex.org/W2981331832","https://openalex.org/W3002504534","https://openalex.org/W3084760804“],”related_works“:[”https://openalex.org/W4363671829","https://openalex.org/W4249005693","https://openalex.org/W3202913553","https://openalex.org/W3194885736","https://openalex.org/W3186512740","https://openalex.org/W3046391934","https://openalex.org/W3017266184","https://openalex.org/W2918377632","https://openalex.org/W2806741695","https://openalex.org/W2669956259“],”abstract_inverted_index“:{”基于异常“:[0,22,44],”检测“:[1,23,45176259359],”是“:[2,52,93100200212],”耦合“:[3],”与“:[4110122153319361],”识别“:[5],”该“:[6,10,16,6410134161601922052082532682852296307323333348352],”不常见“:[7],”到“:[8,14,35,38,411081241551772022262321],”捕获“:[9],”异常“:[11],“活动,”:[12],“和”:[13,67,84118152174256287300310],“查找”:[15],“奇怪”:[17],“动作”:[18],“后面”:[19],“那个”:[20,58,98211267306],“activity”。“:[21],”has“:[24],”a“:[25,54101196215],”wide“:[26],”scope“:[27],”of“:[28,56,60,70,81116129171182284],“critical”:[29],”applications“:[30],”from“:[31,76,85,89207],“bank”:[32],“application”:[33],“security”:[34],“regular”:[36],“sciences”:[37],“medical”:[39],“systems”:[40],“营销”:[42],“应用程序”。“:[43],”采用“:[46168],”由“:[47114],”各种“:[48],”机器“:[49141],”学习“:[50142157],”技术“:[51],”真的“:[53],”类型“:[55],”系统“:[57],”组成“:[59],”人工“:[61],”智能。“:[62],”With“:[63],”evern-expanding“:[65],”volume“:[66],”new“:68],”sorts“:[69],”information“,”:[71],”for“:[72104136169191239],”example“,”:[73],“sensor”:[74],“information”:[75231],“an”:[77],“incontestability”:[78],“surgest”:[79],“amount”:[80],“IoT”:[82],“devices”:[83],”网络“:[86222],“流”:[87],“数据”:[88173210],“云”:[90],“计算,“:[91],“它”:[92],“隐含”:[94],“理解”:[95],“没有”:[96],“惊喜”:[97],“那里”:[99],“发展”:[102],“热情”:[103],“拥有”:[105],“选择”:[107],“交易”:[109],“更多”:[111],“结论”:[112],“自动”:[113],“意味着”:[115],“AI”:[117],“ML”:119276286345],“应用程序”。“:[120],”“但是”:[121],“尊重”:[123320],“异常”:[125206358],“检测”,:[126],“许多”:[127],“应用程序”:[128],“方案”:[131],“是”:[132167224237247],“简单”:[133],“激情”:[135],“探测”。“:[137183],”In“:[138],”this“:[139],”论文“:[140],”(ML)“:[143],”技术“:[144159],”即“:[145],”SVM“:[147],”隔离“:[148308],”森林“:[149],”分类器“:[150314],”实验“:[151],”参考“:[154],”深层“:[1562162220],”(DL)“:[158],”提议“:[161334],”DA-LSTM“:[1162269]335349],“(深度”:[163],“自动编码器”:[164217],“LSTM)“:[165],”模型“:[166270],”预处理“:[170],”日志“:[172209230240354],”基于异常“:[175],”获取“:[178],”更好“:[179204272357],”性能“:[180282323],”度量“:[181],”安“:[184],”增强“:[185],”LSTM“:[186],”(长短期“:[187],”内存“:[188],”建模“:[189],”优化“:[190],“合适”:[193],“参数”:[194],“使用”:[195],“遗传“:[197],”算法“:[198],”(GA),“:[199],”已利用“:[201225],”识别“:[203],”过滤“:[213],”采用“:[214],“(DA)。“:[218],“The”:[219263302],“Neural”:[221],“models”:[223246289],“change”:[227],“over”:[228],“unstructured”:[229],“training”:[233],“ready”:[234],“features”,“:[235],“which”:[236],“reasonal”:[238],“classification”:[241],“in”:[242],“detecting”:[2043],“exceptions”。“:[244],”这些“:[245],”评估“:[248],”利用“:[249],”两个“:[250],”基准“:[251],”数据集“:[252],”开放堆栈“:[254],”日志“:[255],”CIDS-001“:[257328],”入侵“:[258],”Openstack“:[260329353],”服务器“:[261330],”dataset。“:[262],”结果“:[264350],”获得“:[265],”显示“:[266356],”执行“:[271337],”比“:[273342],”其他“:[274362],“显著”:[275363],”技术。“:[277],”我们“:[278],”进一步“:[279],”调查“:[280],”度量“:[283324],”DL“:[288],”通过“:[290],”众所周知“:[292],”指示符“:[293],”测量“:[294],”特别是“:[295],”F-measure“:[297],”准确度“:[298],”召回“:[299],”精度“。“:[301],”探索“:[303],”结论“:[304],”显示“:[305],”森林“:309],”支持“:[311],”向量“:[321],“机器”:[313364],“执行”:[351],,“大致”:[316],“81%和”:[37],“79%准确度”:[338],“度量”:[325]],“大约”:[338],“99.1%”:[33.9],“改进”:[340],“精度”:[341],“熟悉的“:[344]”算法。“:[346],”进一步“,:[347],”数据集“:[355],”比较“:[360],”学习“:[365],”模型。“:[366]},”cited_by_api_url“:”https://api.openalex.org/works?filter=cites:W4200128868“,”counts_by_year“:[],”updated_date“:”2024-09-18T18:56:34.853491“,”created_date:“2021-12-31”}“