{"id":"https://openalex.org/W4411948933","doi":"https://doi.org/10.1109/iwcmc65282.2025.11059531","title":"Multivariate Time Series Anomaly Detection in Cyber Security Systems via Stein Discrepancy","display_name":"Multivariate Time Series Anomaly Detection in Cyber Security Systems via Stein Discrepancy","publication_year":2025,"publication_date":"2025-05-12","ids":{"openalex":"https://openalex.org/W4411948933","doi":"https://doi.org/10.1109/iwcmc65282.2025.11059531"},"language":"en","primary_location":{"id":"doi:10.1109/iwcmc65282.2025.11059531","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc65282.2025.11059531","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Wireless Communications and Mobile Computing (IWCMC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101486334","display_name":"Ge Wang","orcid":"https://orcid.org/0009-0008-6352-3331"},"institutions":[{"id":"https://openalex.org/I98227222","display_name":"China National Petroleum Corporation (China)","ror":"https://ror.org/05269d038","country_code":"CN","type":"company","lineage":["https://openalex.org/I98227222"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wang Ge","raw_affiliation_strings":["CNPC Engineering Technology R&D Company Limited,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CNPC Engineering Technology R&D Company Limited,Beijing,China","institution_ids":["https://openalex.org/I98227222"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5118791801","display_name":"Lv Congyue","orcid":null},"institutions":[{"id":"https://openalex.org/I98227222","display_name":"China National Petroleum Corporation (China)","ror":"https://ror.org/05269d038","country_code":"CN","type":"company","lineage":["https://openalex.org/I98227222"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lv Congyue","raw_affiliation_strings":["CNPC Engineering Technology R&D Company Limited,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CNPC Engineering Technology R&D Company Limited,Beijing,China","institution_ids":["https://openalex.org/I98227222"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5118791802","display_name":"Xing Shiwang","orcid":null},"institutions":[{"id":"https://openalex.org/I98227222","display_name":"China National Petroleum Corporation (China)","ror":"https://ror.org/05269d038","country_code":"CN","type":"company","lineage":["https://openalex.org/I98227222"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xing Shiwang","raw_affiliation_strings":["CNPC Engineering Technology R&D Company Limited,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CNPC Engineering Technology R&D Company Limited,Beijing,China","institution_ids":["https://openalex.org/I98227222"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I98227222"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"961","last_page":"966"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9919999837875366,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9876999855041504,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.7499984502792358},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.7060493230819702},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6807153224945068},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.656842052936554},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5155758261680603},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.48358434438705444},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4203532636165619},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4107182025909424},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3269333839416504},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.26680833101272583},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.10024908185005188}],"concepts":[{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.7499984502792358},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.7060493230819702},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6807153224945068},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.656842052936554},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5155758261680603},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.48358434438705444},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4203532636165619},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4107182025909424},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3269333839416504},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26680833101272583},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.10024908185005188},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwcmc65282.2025.11059531","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc65282.2025.11059531","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Wireless Communications and Mobile Computing (IWCMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1554085250","https://openalex.org/W1788497923","https://openalex.org/W1995443851","https://openalex.org/W2026453187","https://openalex.org/W2062417985","https://openalex.org/W2064675550","https://openalex.org/W2743617586","https://openalex.org/W2785362611","https://openalex.org/W2911200746","https://openalex.org/W2950361482","https://openalex.org/W2965433388","https://openalex.org/W2983192835","https://openalex.org/W3083560262","https://openalex.org/W3096831136","https://openalex.org/W3128634608","https://openalex.org/W3155567600","https://openalex.org/W3169450514","https://openalex.org/W3170937175","https://openalex.org/W3177318507","https://openalex.org/W4283318673","https://openalex.org/W4385245566","https://openalex.org/W6640212811","https://openalex.org/W6720208624","https://openalex.org/W6734551447","https://openalex.org/W6745537798","https://openalex.org/W6753749979","https://openalex.org/W6754814508","https://openalex.org/W6756753118","https://openalex.org/W6762324091","https://openalex.org/W6765775151","https://openalex.org/W6767996718","https://openalex.org/W6798213735","https://openalex.org/W7016021835"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2667207928","https://openalex.org/W2912112202","https://openalex.org/W4377864969","https://openalex.org/W3120251014"],"abstract_inverted_index":{"Recent":[0],"years":[1],"have":[2,37],"witnessed":[3],"significant":[4,176],"advancements":[5],"in":[6,15,24,51,119],"communication":[7],"and":[8,21,46],"computing":[9],"networks,":[10],"which":[11,30],"are":[12,31,62,156],"increasingly":[13,63],"deployed":[14],"Internet":[16],"technology":[17],"applications.":[18],"Continuous":[19],"monitoring":[20],"detecting":[22,117],"anomalies":[23,118],"key":[25],"system":[26],"metrics,":[27],"most":[28],"of":[29,48,93,128,148,179],"multivariate":[32],"time":[33,129],"series":[34],"(MTS)":[35],"data,":[36],"become":[38],"an":[39],"essential":[40],"method.":[41],"Concerning":[42],"the":[43,71,91,102,125,146,164],"continuous":[44],"recording":[45],"cyclicality":[47],"MTS":[49,94,140,155],"data":[50,72,87,95,150,166],"cyber":[52,120],"security":[53,121],"systems,":[54],"Energy-Based":[55],"Models":[56],"(EBMs)":[57],"that":[58],"model":[59],"unnormalized":[60],"densities":[61],"favored":[64],"for":[65,139,168],"their":[66],"generalization":[67],"capabilities.":[68],"Directly":[69],"learn":[70],"distributions":[73],"with":[74],"EBMs":[75],"is":[76],"limited":[77],"by":[78,123],"sampler":[79],"capabilities,":[80],"leading":[81],"to":[82,143],"a":[83,113,134],"focus":[84],"on":[85,116],"estimating":[86,124],"distribution":[88],"gradients.":[89],"However,":[90],"gradient":[92,147],"dynamically":[96],"changes":[97],"over":[98,181],"time.":[99],"To":[100],"address":[101],"issue,":[103],"we":[104,132],"propose":[105],"Anomaly":[106],"Detection":[107],"through":[108,158],"Learned":[109],"Temporal":[110],"Gradient":[111],"(AnoLTG),":[112],"method":[114],"focused":[115],"systems":[122],"temporal":[126],"gradients":[127,167],"series.":[130],"Firstly,":[131],"develop":[133],"network":[135],"incorporates":[136],"noise":[137],"perturbation":[138],"during":[141],"training,":[142],"robustly":[144],"learns":[145],"current":[149],"under":[151],"historical":[152],"conditions.":[153],"Subsequently,":[154],"reconstructed":[157],"Annealed":[159],"Langevin":[160],"Dynamics":[161],"(ALD),":[162],"leveraging":[163],"learned":[165],"precise":[169],"anomaly":[170],"detection.":[171],"Finally,":[172],"extensive":[173],"experiments":[174],"demonstrate":[175],"performance":[177],"improvements":[178],"AnoLTG":[180],"existing":[182],"state-of-the-art":[183],"methods.":[184]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}