Unsupervised Multivariate Time Series Data Anomaly Detection in Industrial IoT: A Confidence Adversarial Autoencoder Network Article Swipe
Jiahao Shan
,
Donghong Cai
,
Fang Fang
,
Zahid Khan
,
Pingzhi Fan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/ojcoms.2024.3511951
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/ojcoms.2024.3511951
Anomaly detection of multivariate time series (MTS) is crucial in industrial intelligent systems. To address the challenges of absence of anomaly labels, fast inference time, multi-source and multi-modality in anomaly detection, researchers have primarily investigated unsupervised reconstruction-driven methods. However, the existing reconstruction-driven methods mainly focus on minimizing reconstruction errors while neglecting the importance of training methods that increase errors between normal and abnormal classes. Furthermore, accurately constructing the feature space of normal and abnormal classes during the reconstruction process remains a challenge. In this paper, we propose an innovative model, namely the confidence adversarial autoencoder (CAAE). The proposed CAAE combines a confidence network, based on window credibility judgment, with an autoencoder to provide credibility support for anomaly detection. We further introduce fake labels to provide the confidence network with a discriminative knowledge for identifying reconstructed data. Additionally, we implement the confidence adversarial training method to generate fake labels to construct an adversarial loss aiming to expand the decision boundary of anomaly scores. Extensive experimental results on publicly available time series datasets are provided to demonstrate the efficiency of our proposed CAAE. It reveals that excellent generalization ability and superior average performance are achieved on different datasets compared with the state-of-the-art methods.
Related Topics
Concepts
Autoencoder
Anomaly detection
Multivariate statistics
Computer science
Series (stratigraphy)
Anomaly (physics)
Time series
Artificial intelligence
Data mining
Confidence interval
Adversarial system
Machine learning
Pattern recognition (psychology)
Statistics
Artificial neural network
Mathematics
Geology
Condensed matter physics
Physics
Paleontology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/ojcoms.2024.3511951
- OA Status
- gold
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405022310
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4405022310Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/ojcoms.2024.3511951Digital Object Identifier
- Title
-
Unsupervised Multivariate Time Series Data Anomaly Detection in Industrial IoT: A Confidence Adversarial Autoencoder NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Jiahao Shan, Donghong Cai, Fang Fang, Zahid Khan, Pingzhi FanList of authors in order
- Landing page
-
https://doi.org/10.1109/ojcoms.2024.3511951Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1109/ojcoms.2024.3511951Direct OA link when available
- Concepts
-
Autoencoder, Anomaly detection, Multivariate statistics, Computer science, Series (stratigraphy), Anomaly (physics), Time series, Artificial intelligence, Data mining, Confidence interval, Adversarial system, Machine learning, Pattern recognition (psychology), Statistics, Artificial neural network, Mathematics, Geology, Condensed matter physics, Physics, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4405022310 |
|---|---|
| doi | https://doi.org/10.1109/ojcoms.2024.3511951 |
| ids.doi | https://doi.org/10.1109/ojcoms.2024.3511951 |
| ids.openalex | https://openalex.org/W4405022310 |
| fwci | 0.0 |
| type | article |
| title | Unsupervised Multivariate Time Series Data Anomaly Detection in Industrial IoT: A Confidence Adversarial Autoencoder Network |
| biblio.issue | |
| biblio.volume | 5 |
| biblio.last_page | 7766 |
| biblio.first_page | 7752 |
| topics[0].id | https://openalex.org/T11512 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9994000196456909 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Anomaly Detection Techniques and Applications |
| topics[1].id | https://openalex.org/T12205 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9789999723434448 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1711 |
| topics[1].subfield.display_name | Signal Processing |
| topics[1].display_name | Time Series Analysis and Forecasting |
| topics[2].id | https://openalex.org/T10400 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9605000019073486 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1705 |
| topics[2].subfield.display_name | Computer Networks and Communications |
| topics[2].display_name | Network Security and Intrusion Detection |
| is_xpac | False |
| apc_list.value | 1750 |
| apc_list.currency | USD |
| apc_list.value_usd | 1750 |
| apc_paid.value | 1750 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 1750 |
| concepts[0].id | https://openalex.org/C101738243 |
| concepts[0].level | 3 |
| concepts[0].score | 0.8593054413795471 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q786435 |
| concepts[0].display_name | Autoencoder |
| concepts[1].id | https://openalex.org/C739882 |
| concepts[1].level | 2 |
| concepts[1].score | 0.8025372624397278 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q3560506 |
| concepts[1].display_name | Anomaly detection |
| concepts[2].id | https://openalex.org/C161584116 |
| concepts[2].level | 2 |
| concepts[2].score | 0.7422246932983398 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1952580 |
| concepts[2].display_name | Multivariate statistics |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.6062560081481934 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C143724316 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5643582940101624 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q312468 |
| concepts[4].display_name | Series (stratigraphy) |
| concepts[5].id | https://openalex.org/C12997251 |
| concepts[5].level | 2 |
| concepts[5].score | 0.546295702457428 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q567560 |
| concepts[5].display_name | Anomaly (physics) |
| concepts[6].id | https://openalex.org/C151406439 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5434775948524475 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q186588 |
| concepts[6].display_name | Time series |
| concepts[7].id | https://openalex.org/C154945302 |
| concepts[7].level | 1 |
| concepts[7].score | 0.5329136848449707 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[7].display_name | Artificial intelligence |
| concepts[8].id | https://openalex.org/C124101348 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4815793037414551 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[8].display_name | Data mining |
| concepts[9].id | https://openalex.org/C44249647 |
| concepts[9].level | 2 |
| concepts[9].score | 0.47558820247650146 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q208498 |
| concepts[9].display_name | Confidence interval |
| concepts[10].id | https://openalex.org/C37736160 |
| concepts[10].level | 2 |
| concepts[10].score | 0.4731489419937134 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q1801315 |
| concepts[10].display_name | Adversarial system |
| concepts[11].id | https://openalex.org/C119857082 |
| concepts[11].level | 1 |
| concepts[11].score | 0.37857577204704285 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[11].display_name | Machine learning |
| concepts[12].id | https://openalex.org/C153180895 |
| concepts[12].level | 2 |
| concepts[12].score | 0.37426435947418213 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[12].display_name | Pattern recognition (psychology) |
| concepts[13].id | https://openalex.org/C105795698 |
| concepts[13].level | 1 |
| concepts[13].score | 0.25671133399009705 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[13].display_name | Statistics |
| concepts[14].id | https://openalex.org/C50644808 |
| concepts[14].level | 2 |
| concepts[14].score | 0.23979169130325317 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[14].display_name | Artificial neural network |
| concepts[15].id | https://openalex.org/C33923547 |
| concepts[15].level | 0 |
| concepts[15].score | 0.18280503153800964 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[15].display_name | Mathematics |
| concepts[16].id | https://openalex.org/C127313418 |
| concepts[16].level | 0 |
| concepts[16].score | 0.10014450550079346 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[16].display_name | Geology |
| concepts[17].id | https://openalex.org/C26873012 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q214781 |
| concepts[17].display_name | Condensed matter physics |
| concepts[18].id | https://openalex.org/C121332964 |
| concepts[18].level | 0 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[18].display_name | Physics |
| concepts[19].id | https://openalex.org/C151730666 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q7205 |
| concepts[19].display_name | Paleontology |
| keywords[0].id | https://openalex.org/keywords/autoencoder |
| keywords[0].score | 0.8593054413795471 |
| keywords[0].display_name | Autoencoder |
| keywords[1].id | https://openalex.org/keywords/anomaly-detection |
| keywords[1].score | 0.8025372624397278 |
| keywords[1].display_name | Anomaly detection |
| keywords[2].id | https://openalex.org/keywords/multivariate-statistics |
| keywords[2].score | 0.7422246932983398 |
| keywords[2].display_name | Multivariate statistics |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.6062560081481934 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/series |
| keywords[4].score | 0.5643582940101624 |
| keywords[4].display_name | Series (stratigraphy) |
| keywords[5].id | https://openalex.org/keywords/anomaly |
| keywords[5].score | 0.546295702457428 |
| keywords[5].display_name | Anomaly (physics) |
| keywords[6].id | https://openalex.org/keywords/time-series |
| keywords[6].score | 0.5434775948524475 |
| keywords[6].display_name | Time series |
| keywords[7].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[7].score | 0.5329136848449707 |
| keywords[7].display_name | Artificial intelligence |
| keywords[8].id | https://openalex.org/keywords/data-mining |
| keywords[8].score | 0.4815793037414551 |
| keywords[8].display_name | Data mining |
| keywords[9].id | https://openalex.org/keywords/confidence-interval |
| keywords[9].score | 0.47558820247650146 |
| keywords[9].display_name | Confidence interval |
| keywords[10].id | https://openalex.org/keywords/adversarial-system |
| keywords[10].score | 0.4731489419937134 |
| keywords[10].display_name | Adversarial system |
| keywords[11].id | https://openalex.org/keywords/machine-learning |
| keywords[11].score | 0.37857577204704285 |
| keywords[11].display_name | Machine learning |
| keywords[12].id | https://openalex.org/keywords/pattern-recognition |
| keywords[12].score | 0.37426435947418213 |
| keywords[12].display_name | Pattern recognition (psychology) |
| keywords[13].id | https://openalex.org/keywords/statistics |
| keywords[13].score | 0.25671133399009705 |
| keywords[13].display_name | Statistics |
| keywords[14].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[14].score | 0.23979169130325317 |
| keywords[14].display_name | Artificial neural network |
| keywords[15].id | https://openalex.org/keywords/mathematics |
| keywords[15].score | 0.18280503153800964 |
| keywords[15].display_name | Mathematics |
| keywords[16].id | https://openalex.org/keywords/geology |
| keywords[16].score | 0.10014450550079346 |
| keywords[16].display_name | Geology |
| language | en |
| locations[0].id | doi:10.1109/ojcoms.2024.3511951 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210202420 |
| locations[0].source.issn | 2644-125X |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2644-125X |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | IEEE Open Journal of the Communications Society |
| locations[0].source.host_organization | https://openalex.org/P4310316002 |
| locations[0].source.host_organization_name | IEEE Communications Society |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310316002, https://openalex.org/P4310319808 |
| locations[0].source.host_organization_lineage_names | IEEE Communications Society, Institute of Electrical and Electronics Engineers |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | IEEE Open Journal of the Communications Society |
| locations[0].landing_page_url | https://doi.org/10.1109/ojcoms.2024.3511951 |
| locations[1].id | pmh:oai:doaj.org/article:ba9222edf0de4d18a9ad7c8325cdcf77 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | IEEE Open Journal of the Communications Society, Vol 5, Pp 7752-7766 (2024) |
| locations[1].landing_page_url | https://doaj.org/article/ba9222edf0de4d18a9ad7c8325cdcf77 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5016737406 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-7941-9933 |
| authorships[0].author.display_name | Jiahao Shan |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I159948400 |
| authorships[0].affiliations[0].raw_affiliation_string | The College of Cyber Security, Jinan University, Guangzhou, China |
| authorships[0].institutions[0].id | https://openalex.org/I159948400 |
| authorships[0].institutions[0].ror | https://ror.org/02xe5ns62 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I159948400 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Jinan University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Jiahao Shan |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | The College of Cyber Security, Jinan University, Guangzhou, China |
| authorships[1].author.id | https://openalex.org/A5008996322 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-5350-135X |
| authorships[1].author.display_name | Donghong Cai |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I159948400 |
| authorships[1].affiliations[0].raw_affiliation_string | The College of Cyber Security, Jinan University, Guangzhou, China |
| authorships[1].institutions[0].id | https://openalex.org/I159948400 |
| authorships[1].institutions[0].ror | https://ror.org/02xe5ns62 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I159948400 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Jinan University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Donghong Cai |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | The College of Cyber Security, Jinan University, Guangzhou, China |
| authorships[2].author.id | https://openalex.org/A5100386867 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-6582-6570 |
| authorships[2].author.display_name | Fang Fang |
| authorships[2].countries | CA |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I125749732 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Electrical and Computer Engineering, Western University, London, Canada |
| authorships[2].institutions[0].id | https://openalex.org/I125749732 |
| authorships[2].institutions[0].ror | https://ror.org/02grkyz14 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I125749732 |
| authorships[2].institutions[0].country_code | CA |
| authorships[2].institutions[0].display_name | Western University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Fang Fang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Electrical and Computer Engineering, Western University, London, Canada |
| authorships[3].author.id | https://openalex.org/A5018592446 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-4710-4010 |
| authorships[3].author.display_name | Zahid Khan |
| authorships[3].countries | SA |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I142024983 |
| authorships[3].affiliations[0].raw_affiliation_string | College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia |
| authorships[3].institutions[0].id | https://openalex.org/I142024983 |
| authorships[3].institutions[0].ror | https://ror.org/053mqrf26 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I142024983 |
| authorships[3].institutions[0].country_code | SA |
| authorships[3].institutions[0].display_name | Prince Sultan University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Zahid Khan |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia |
| authorships[4].author.id | https://openalex.org/A5101880047 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-8281-6251 |
| authorships[4].author.display_name | Pingzhi Fan |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4800084 |
| authorships[4].affiliations[0].raw_affiliation_string | Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University, Chengdu, China |
| authorships[4].institutions[0].id | https://openalex.org/I4800084 |
| authorships[4].institutions[0].ror | https://ror.org/00hn7w693 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I4800084 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Southwest Jiaotong University |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Pingzhi Fan |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University, Chengdu, China |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1109/ojcoms.2024.3511951 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Unsupervised Multivariate Time Series Data Anomaly Detection in Industrial IoT: A Confidence Adversarial Autoencoder Network |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11512 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9994000196456909 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Anomaly Detection Techniques and Applications |
| related_works | https://openalex.org/W3186512740, https://openalex.org/W4363671829, 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 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1109/ojcoms.2024.3511951 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210202420 |
| best_oa_location.source.issn | 2644-125X |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2644-125X |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | IEEE Open Journal of the Communications Society |
| best_oa_location.source.host_organization | https://openalex.org/P4310316002 |
| best_oa_location.source.host_organization_name | IEEE Communications Society |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310316002, https://openalex.org/P4310319808 |
| best_oa_location.source.host_organization_lineage_names | IEEE Communications Society, Institute of Electrical and Electronics Engineers |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | IEEE Open Journal of the Communications Society |
| best_oa_location.landing_page_url | https://doi.org/10.1109/ojcoms.2024.3511951 |
| primary_location.id | doi:10.1109/ojcoms.2024.3511951 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210202420 |
| primary_location.source.issn | 2644-125X |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2644-125X |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | IEEE Open Journal of the Communications Society |
| primary_location.source.host_organization | https://openalex.org/P4310316002 |
| primary_location.source.host_organization_name | IEEE Communications Society |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310316002, https://openalex.org/P4310319808 |
| primary_location.source.host_organization_lineage_names | IEEE Communications Society, Institute of Electrical and Electronics Engineers |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | IEEE Open Journal of the Communications Society |
| primary_location.landing_page_url | https://doi.org/10.1109/ojcoms.2024.3511951 |
| publication_date | 2024-01-01 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2964248614, https://openalex.org/W4313479509, https://openalex.org/W6758101687, https://openalex.org/W6748102297, https://openalex.org/W2786827964, https://openalex.org/W2950361482, https://openalex.org/W3081497074, https://openalex.org/W3157328277, https://openalex.org/W4283318673, https://openalex.org/W6845625448, https://openalex.org/W4239510810, https://openalex.org/W2021660846, https://openalex.org/W2089468765, https://openalex.org/W1788497923, https://openalex.org/W3206674745, https://openalex.org/W4367016885, https://openalex.org/W3192519096, https://openalex.org/W1498436455, https://openalex.org/W6864715598, https://openalex.org/W3170937175, https://openalex.org/W2791879367, https://openalex.org/W4283205333, https://openalex.org/W2784032999, https://openalex.org/W4226082223, https://openalex.org/W3086419524, https://openalex.org/W4225526013, https://openalex.org/W4319066192, https://openalex.org/W4382203079, https://openalex.org/W4285299229, https://openalex.org/W4388469868, https://openalex.org/W4312384107, https://openalex.org/W3004999940, https://openalex.org/W3123700361, https://openalex.org/W2743617586, https://openalex.org/W2768947629, https://openalex.org/W3127386226, https://openalex.org/W3165716503, https://openalex.org/W4389545318 |
| referenced_works_count | 38 |
| abstract_inverted_index.a | 80, 100, 129 |
| abstract_inverted_index.In | 82 |
| abstract_inverted_index.It | 181 |
| abstract_inverted_index.To | 13 |
| abstract_inverted_index.We | 118 |
| abstract_inverted_index.an | 87, 109, 150 |
| abstract_inverted_index.in | 9, 28 |
| abstract_inverted_index.is | 7 |
| abstract_inverted_index.of | 2, 17, 19, 53, 70, 159, 177 |
| abstract_inverted_index.on | 45, 104, 165, 193 |
| abstract_inverted_index.to | 111, 123, 144, 148, 154, 173 |
| abstract_inverted_index.we | 85, 137 |
| abstract_inverted_index.The | 96 |
| abstract_inverted_index.and | 26, 61, 72, 187 |
| abstract_inverted_index.are | 171, 191 |
| abstract_inverted_index.for | 115, 132 |
| abstract_inverted_index.our | 178 |
| abstract_inverted_index.the | 15, 39, 51, 67, 76, 91, 125, 139, 156, 175, 198 |
| abstract_inverted_index.CAAE | 98 |
| abstract_inverted_index.fake | 121, 146 |
| abstract_inverted_index.fast | 22 |
| abstract_inverted_index.have | 32 |
| abstract_inverted_index.loss | 152 |
| abstract_inverted_index.that | 56, 183 |
| abstract_inverted_index.this | 83 |
| abstract_inverted_index.time | 4, 168 |
| abstract_inverted_index.with | 108, 128, 197 |
| abstract_inverted_index.(MTS) | 6 |
| abstract_inverted_index.CAAE. | 180 |
| abstract_inverted_index.based | 103 |
| abstract_inverted_index.data. | 135 |
| abstract_inverted_index.focus | 44 |
| abstract_inverted_index.space | 69 |
| abstract_inverted_index.time, | 24 |
| abstract_inverted_index.while | 49 |
| abstract_inverted_index.aiming | 153 |
| abstract_inverted_index.during | 75 |
| abstract_inverted_index.errors | 48, 58 |
| abstract_inverted_index.expand | 155 |
| abstract_inverted_index.labels | 122, 147 |
| abstract_inverted_index.mainly | 43 |
| abstract_inverted_index.method | 143 |
| abstract_inverted_index.model, | 89 |
| abstract_inverted_index.namely | 90 |
| abstract_inverted_index.normal | 60, 71 |
| abstract_inverted_index.paper, | 84 |
| abstract_inverted_index.series | 5, 169 |
| abstract_inverted_index.window | 105 |
| abstract_inverted_index.(CAAE). | 95 |
| abstract_inverted_index.Anomaly | 0 |
| abstract_inverted_index.ability | 186 |
| abstract_inverted_index.absence | 18 |
| abstract_inverted_index.address | 14 |
| abstract_inverted_index.anomaly | 20, 29, 116, 160 |
| abstract_inverted_index.average | 189 |
| abstract_inverted_index.between | 59 |
| abstract_inverted_index.classes | 74 |
| abstract_inverted_index.crucial | 8 |
| abstract_inverted_index.feature | 68 |
| abstract_inverted_index.further | 119 |
| abstract_inverted_index.labels, | 21 |
| abstract_inverted_index.methods | 42, 55 |
| abstract_inverted_index.network | 127 |
| abstract_inverted_index.process | 78 |
| abstract_inverted_index.propose | 86 |
| abstract_inverted_index.provide | 112, 124 |
| abstract_inverted_index.remains | 79 |
| abstract_inverted_index.results | 164 |
| abstract_inverted_index.reveals | 182 |
| abstract_inverted_index.scores. | 161 |
| abstract_inverted_index.support | 114 |
| abstract_inverted_index.However, | 38 |
| abstract_inverted_index.abnormal | 62, 73 |
| abstract_inverted_index.achieved | 192 |
| abstract_inverted_index.boundary | 158 |
| abstract_inverted_index.classes. | 63 |
| abstract_inverted_index.combines | 99 |
| abstract_inverted_index.compared | 196 |
| abstract_inverted_index.datasets | 170, 195 |
| abstract_inverted_index.decision | 157 |
| abstract_inverted_index.existing | 40 |
| abstract_inverted_index.generate | 145 |
| abstract_inverted_index.increase | 57 |
| abstract_inverted_index.methods. | 37, 200 |
| abstract_inverted_index.network, | 102 |
| abstract_inverted_index.proposed | 97, 179 |
| abstract_inverted_index.provided | 172 |
| abstract_inverted_index.publicly | 166 |
| abstract_inverted_index.superior | 188 |
| abstract_inverted_index.systems. | 12 |
| abstract_inverted_index.training | 54, 142 |
| abstract_inverted_index.Extensive | 162 |
| abstract_inverted_index.available | 167 |
| abstract_inverted_index.construct | 149 |
| abstract_inverted_index.detection | 1 |
| abstract_inverted_index.different | 194 |
| abstract_inverted_index.excellent | 184 |
| abstract_inverted_index.implement | 138 |
| abstract_inverted_index.inference | 23 |
| abstract_inverted_index.introduce | 120 |
| abstract_inverted_index.judgment, | 107 |
| abstract_inverted_index.knowledge | 131 |
| abstract_inverted_index.primarily | 33 |
| abstract_inverted_index.accurately | 65 |
| abstract_inverted_index.challenge. | 81 |
| abstract_inverted_index.challenges | 16 |
| abstract_inverted_index.confidence | 92, 101, 126, 140 |
| abstract_inverted_index.detection, | 30 |
| abstract_inverted_index.detection. | 117 |
| abstract_inverted_index.efficiency | 176 |
| abstract_inverted_index.importance | 52 |
| abstract_inverted_index.industrial | 10 |
| abstract_inverted_index.innovative | 88 |
| abstract_inverted_index.minimizing | 46 |
| abstract_inverted_index.neglecting | 50 |
| abstract_inverted_index.adversarial | 93, 141, 151 |
| abstract_inverted_index.autoencoder | 94, 110 |
| abstract_inverted_index.credibility | 106, 113 |
| abstract_inverted_index.demonstrate | 174 |
| abstract_inverted_index.identifying | 133 |
| abstract_inverted_index.intelligent | 11 |
| abstract_inverted_index.performance | 190 |
| abstract_inverted_index.researchers | 31 |
| abstract_inverted_index.Furthermore, | 64 |
| abstract_inverted_index.constructing | 66 |
| abstract_inverted_index.experimental | 163 |
| abstract_inverted_index.investigated | 34 |
| abstract_inverted_index.multi-source | 25 |
| abstract_inverted_index.multivariate | 3 |
| abstract_inverted_index.unsupervised | 35 |
| abstract_inverted_index.Additionally, | 136 |
| abstract_inverted_index.reconstructed | 134 |
| abstract_inverted_index.discriminative | 130 |
| abstract_inverted_index.generalization | 185 |
| abstract_inverted_index.multi-modality | 27 |
| abstract_inverted_index.reconstruction | 47, 77 |
| abstract_inverted_index.state-of-the-art | 199 |
| abstract_inverted_index.reconstruction-driven | 36, 41 |
| cited_by_percentile_year | |
| countries_distinct_count | 3 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.24801304 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |