Generative Adversarial Network-Based Anomaly Detection and Forecasting with Unlabeled Data for 5G Vertical Applications
With the development of 5G vertical applications, a huge amount of unlabeled network data can be collected, which can be employed for evaluating the user experience and network operation status, such as the identifications and predictions of network anomalies. However, it is challenging to achieve h...
Ausführliche Beschreibung
Autor*in: |
Qing Zhang [verfasserIn] Bin Chen [verfasserIn] Taoye Zhang [verfasserIn] Kang Cao [verfasserIn] Yuming Ding [verfasserIn] Tianhang Gao [verfasserIn] Zhongyuan Zhao [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2023 |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
In: Applied Sciences - MDPI AG, 2012, 13(2023), 10745, p 10745 |
---|---|
Übergeordnetes Werk: |
volume:13 ; year:2023 ; number:10745, p 10745 |
Links: |
---|
DOI / URN: |
10.3390/app131910745 |
---|
Katalog-ID: |
DOAJ093245246 |
---|
LEADER | 01000naa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ093245246 | ||
003 | DE-627 | ||
005 | 20240413160918.0 | ||
007 | cr uuu---uuuuu | ||
008 | 240413s2023 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.3390/app131910745 |2 doi | |
035 | |a (DE-627)DOAJ093245246 | ||
035 | |a (DE-599)DOAJafaa1d586a57414ea1a35cc4515b4015 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
050 | 0 | |a TA1-2040 | |
050 | 0 | |a QH301-705.5 | |
050 | 0 | |a QC1-999 | |
050 | 0 | |a QD1-999 | |
100 | 0 | |a Qing Zhang |e verfasserin |4 aut | |
245 | 1 | 0 | |a Generative Adversarial Network-Based Anomaly Detection and Forecasting with Unlabeled Data for 5G Vertical Applications |
264 | 1 | |c 2023 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a With the development of 5G vertical applications, a huge amount of unlabeled network data can be collected, which can be employed for evaluating the user experience and network operation status, such as the identifications and predictions of network anomalies. However, it is challenging to achieve highly accurate evaluation results using the conventional statistical methods due to the limitations of data quality. In this paper, generative adversarial network (GAN)-based anomaly detection and forecasting are studied for 5G vertical applications, which can provide considerable detection and prediction results with unlabeled network data samples. First, the paradigm and deployment of the deep-learning-based anomaly detection and forecasting scheme are designed. Second, the network structure and the training strategy are introduced to fully explore the potential of the GAN model. Finally, the experimental results of our proposed GAN model are provided based on the practical unlabeled network operation data in various 5G vertical scenarios, which show that our proposed scheme can achieve significant performance gains for network anomaly detection and forecasting. | ||
650 | 4 | |a anomaly detection and forecasting | |
650 | 4 | |a 5G vertical application | |
650 | 4 | |a unlabeled data | |
650 | 4 | |a network quality | |
650 | 4 | |a neural networks | |
653 | 0 | |a Technology | |
653 | 0 | |a T | |
653 | 0 | |a Engineering (General). Civil engineering (General) | |
653 | 0 | |a Biology (General) | |
653 | 0 | |a Physics | |
653 | 0 | |a Chemistry | |
700 | 0 | |a Bin Chen |e verfasserin |4 aut | |
700 | 0 | |a Taoye Zhang |e verfasserin |4 aut | |
700 | 0 | |a Kang Cao |e verfasserin |4 aut | |
700 | 0 | |a Yuming Ding |e verfasserin |4 aut | |
700 | 0 | |a Tianhang Gao |e verfasserin |4 aut | |
700 | 0 | |a Zhongyuan Zhao |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t Applied Sciences |d MDPI AG, 2012 |g 13(2023), 10745, p 10745 |w (DE-627)737287640 |w (DE-600)2704225-X |x 20763417 |7 nnns |
773 | 1 | 8 | |g volume:13 |g year:2023 |g number:10745, p 10745 |
856 | 4 | 0 | |u https://doi.org/10.3390/app131910745 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/afaa1d586a57414ea1a35cc4515b4015 |z kostenfrei |
856 | 4 | 0 | |u https://www.mdpi.com/2076-3417/13/19/10745 |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/2076-3417 |y Journal toc |z kostenfrei |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_DOAJ | ||
912 | |a GBV_ILN_11 | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_171 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_370 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_2055 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4335 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 13 |j 2023 |e 10745, p 10745 |
author_variant |
q z qz b c bc t z tz k c kc y d yd t g tg z z zz |
---|---|
matchkey_str |
article:20763417:2023----::eeaiedesrantokaeaoayeetoadoeatnwtulblda |
hierarchy_sort_str |
2023 |
callnumber-subject-code |
TA |
publishDate |
2023 |
allfields |
10.3390/app131910745 doi (DE-627)DOAJ093245246 (DE-599)DOAJafaa1d586a57414ea1a35cc4515b4015 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Qing Zhang verfasserin aut Generative Adversarial Network-Based Anomaly Detection and Forecasting with Unlabeled Data for 5G Vertical Applications 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the development of 5G vertical applications, a huge amount of unlabeled network data can be collected, which can be employed for evaluating the user experience and network operation status, such as the identifications and predictions of network anomalies. However, it is challenging to achieve highly accurate evaluation results using the conventional statistical methods due to the limitations of data quality. In this paper, generative adversarial network (GAN)-based anomaly detection and forecasting are studied for 5G vertical applications, which can provide considerable detection and prediction results with unlabeled network data samples. First, the paradigm and deployment of the deep-learning-based anomaly detection and forecasting scheme are designed. Second, the network structure and the training strategy are introduced to fully explore the potential of the GAN model. Finally, the experimental results of our proposed GAN model are provided based on the practical unlabeled network operation data in various 5G vertical scenarios, which show that our proposed scheme can achieve significant performance gains for network anomaly detection and forecasting. anomaly detection and forecasting 5G vertical application unlabeled data network quality neural networks Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Bin Chen verfasserin aut Taoye Zhang verfasserin aut Kang Cao verfasserin aut Yuming Ding verfasserin aut Tianhang Gao verfasserin aut Zhongyuan Zhao verfasserin aut In Applied Sciences MDPI AG, 2012 13(2023), 10745, p 10745 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:13 year:2023 number:10745, p 10745 https://doi.org/10.3390/app131910745 kostenfrei https://doaj.org/article/afaa1d586a57414ea1a35cc4515b4015 kostenfrei https://www.mdpi.com/2076-3417/13/19/10745 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 10745, p 10745 |
spelling |
10.3390/app131910745 doi (DE-627)DOAJ093245246 (DE-599)DOAJafaa1d586a57414ea1a35cc4515b4015 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Qing Zhang verfasserin aut Generative Adversarial Network-Based Anomaly Detection and Forecasting with Unlabeled Data for 5G Vertical Applications 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the development of 5G vertical applications, a huge amount of unlabeled network data can be collected, which can be employed for evaluating the user experience and network operation status, such as the identifications and predictions of network anomalies. However, it is challenging to achieve highly accurate evaluation results using the conventional statistical methods due to the limitations of data quality. In this paper, generative adversarial network (GAN)-based anomaly detection and forecasting are studied for 5G vertical applications, which can provide considerable detection and prediction results with unlabeled network data samples. First, the paradigm and deployment of the deep-learning-based anomaly detection and forecasting scheme are designed. Second, the network structure and the training strategy are introduced to fully explore the potential of the GAN model. Finally, the experimental results of our proposed GAN model are provided based on the practical unlabeled network operation data in various 5G vertical scenarios, which show that our proposed scheme can achieve significant performance gains for network anomaly detection and forecasting. anomaly detection and forecasting 5G vertical application unlabeled data network quality neural networks Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Bin Chen verfasserin aut Taoye Zhang verfasserin aut Kang Cao verfasserin aut Yuming Ding verfasserin aut Tianhang Gao verfasserin aut Zhongyuan Zhao verfasserin aut In Applied Sciences MDPI AG, 2012 13(2023), 10745, p 10745 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:13 year:2023 number:10745, p 10745 https://doi.org/10.3390/app131910745 kostenfrei https://doaj.org/article/afaa1d586a57414ea1a35cc4515b4015 kostenfrei https://www.mdpi.com/2076-3417/13/19/10745 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 10745, p 10745 |
allfields_unstemmed |
10.3390/app131910745 doi (DE-627)DOAJ093245246 (DE-599)DOAJafaa1d586a57414ea1a35cc4515b4015 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Qing Zhang verfasserin aut Generative Adversarial Network-Based Anomaly Detection and Forecasting with Unlabeled Data for 5G Vertical Applications 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the development of 5G vertical applications, a huge amount of unlabeled network data can be collected, which can be employed for evaluating the user experience and network operation status, such as the identifications and predictions of network anomalies. However, it is challenging to achieve highly accurate evaluation results using the conventional statistical methods due to the limitations of data quality. In this paper, generative adversarial network (GAN)-based anomaly detection and forecasting are studied for 5G vertical applications, which can provide considerable detection and prediction results with unlabeled network data samples. First, the paradigm and deployment of the deep-learning-based anomaly detection and forecasting scheme are designed. Second, the network structure and the training strategy are introduced to fully explore the potential of the GAN model. Finally, the experimental results of our proposed GAN model are provided based on the practical unlabeled network operation data in various 5G vertical scenarios, which show that our proposed scheme can achieve significant performance gains for network anomaly detection and forecasting. anomaly detection and forecasting 5G vertical application unlabeled data network quality neural networks Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Bin Chen verfasserin aut Taoye Zhang verfasserin aut Kang Cao verfasserin aut Yuming Ding verfasserin aut Tianhang Gao verfasserin aut Zhongyuan Zhao verfasserin aut In Applied Sciences MDPI AG, 2012 13(2023), 10745, p 10745 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:13 year:2023 number:10745, p 10745 https://doi.org/10.3390/app131910745 kostenfrei https://doaj.org/article/afaa1d586a57414ea1a35cc4515b4015 kostenfrei https://www.mdpi.com/2076-3417/13/19/10745 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 10745, p 10745 |
allfieldsGer |
10.3390/app131910745 doi (DE-627)DOAJ093245246 (DE-599)DOAJafaa1d586a57414ea1a35cc4515b4015 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Qing Zhang verfasserin aut Generative Adversarial Network-Based Anomaly Detection and Forecasting with Unlabeled Data for 5G Vertical Applications 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the development of 5G vertical applications, a huge amount of unlabeled network data can be collected, which can be employed for evaluating the user experience and network operation status, such as the identifications and predictions of network anomalies. However, it is challenging to achieve highly accurate evaluation results using the conventional statistical methods due to the limitations of data quality. In this paper, generative adversarial network (GAN)-based anomaly detection and forecasting are studied for 5G vertical applications, which can provide considerable detection and prediction results with unlabeled network data samples. First, the paradigm and deployment of the deep-learning-based anomaly detection and forecasting scheme are designed. Second, the network structure and the training strategy are introduced to fully explore the potential of the GAN model. Finally, the experimental results of our proposed GAN model are provided based on the practical unlabeled network operation data in various 5G vertical scenarios, which show that our proposed scheme can achieve significant performance gains for network anomaly detection and forecasting. anomaly detection and forecasting 5G vertical application unlabeled data network quality neural networks Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Bin Chen verfasserin aut Taoye Zhang verfasserin aut Kang Cao verfasserin aut Yuming Ding verfasserin aut Tianhang Gao verfasserin aut Zhongyuan Zhao verfasserin aut In Applied Sciences MDPI AG, 2012 13(2023), 10745, p 10745 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:13 year:2023 number:10745, p 10745 https://doi.org/10.3390/app131910745 kostenfrei https://doaj.org/article/afaa1d586a57414ea1a35cc4515b4015 kostenfrei https://www.mdpi.com/2076-3417/13/19/10745 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 10745, p 10745 |
allfieldsSound |
10.3390/app131910745 doi (DE-627)DOAJ093245246 (DE-599)DOAJafaa1d586a57414ea1a35cc4515b4015 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Qing Zhang verfasserin aut Generative Adversarial Network-Based Anomaly Detection and Forecasting with Unlabeled Data for 5G Vertical Applications 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the development of 5G vertical applications, a huge amount of unlabeled network data can be collected, which can be employed for evaluating the user experience and network operation status, such as the identifications and predictions of network anomalies. However, it is challenging to achieve highly accurate evaluation results using the conventional statistical methods due to the limitations of data quality. In this paper, generative adversarial network (GAN)-based anomaly detection and forecasting are studied for 5G vertical applications, which can provide considerable detection and prediction results with unlabeled network data samples. First, the paradigm and deployment of the deep-learning-based anomaly detection and forecasting scheme are designed. Second, the network structure and the training strategy are introduced to fully explore the potential of the GAN model. Finally, the experimental results of our proposed GAN model are provided based on the practical unlabeled network operation data in various 5G vertical scenarios, which show that our proposed scheme can achieve significant performance gains for network anomaly detection and forecasting. anomaly detection and forecasting 5G vertical application unlabeled data network quality neural networks Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Bin Chen verfasserin aut Taoye Zhang verfasserin aut Kang Cao verfasserin aut Yuming Ding verfasserin aut Tianhang Gao verfasserin aut Zhongyuan Zhao verfasserin aut In Applied Sciences MDPI AG, 2012 13(2023), 10745, p 10745 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:13 year:2023 number:10745, p 10745 https://doi.org/10.3390/app131910745 kostenfrei https://doaj.org/article/afaa1d586a57414ea1a35cc4515b4015 kostenfrei https://www.mdpi.com/2076-3417/13/19/10745 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 10745, p 10745 |
language |
English |
source |
In Applied Sciences 13(2023), 10745, p 10745 volume:13 year:2023 number:10745, p 10745 |
sourceStr |
In Applied Sciences 13(2023), 10745, p 10745 volume:13 year:2023 number:10745, p 10745 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
anomaly detection and forecasting 5G vertical application unlabeled data network quality neural networks Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry |
isfreeaccess_bool |
true |
container_title |
Applied Sciences |
authorswithroles_txt_mv |
Qing Zhang @@aut@@ Bin Chen @@aut@@ Taoye Zhang @@aut@@ Kang Cao @@aut@@ Yuming Ding @@aut@@ Tianhang Gao @@aut@@ Zhongyuan Zhao @@aut@@ |
publishDateDaySort_date |
2023-01-01T00:00:00Z |
hierarchy_top_id |
737287640 |
id |
DOAJ093245246 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ093245246</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413160918.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240413s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/app131910745</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ093245246</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJafaa1d586a57414ea1a35cc4515b4015</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TA1-2040</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QH301-705.5</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QC1-999</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QD1-999</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Qing Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Generative Adversarial Network-Based Anomaly Detection and Forecasting with Unlabeled Data for 5G Vertical Applications</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">With the development of 5G vertical applications, a huge amount of unlabeled network data can be collected, which can be employed for evaluating the user experience and network operation status, such as the identifications and predictions of network anomalies. However, it is challenging to achieve highly accurate evaluation results using the conventional statistical methods due to the limitations of data quality. In this paper, generative adversarial network (GAN)-based anomaly detection and forecasting are studied for 5G vertical applications, which can provide considerable detection and prediction results with unlabeled network data samples. First, the paradigm and deployment of the deep-learning-based anomaly detection and forecasting scheme are designed. Second, the network structure and the training strategy are introduced to fully explore the potential of the GAN model. Finally, the experimental results of our proposed GAN model are provided based on the practical unlabeled network operation data in various 5G vertical scenarios, which show that our proposed scheme can achieve significant performance gains for network anomaly detection and forecasting.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">anomaly detection and forecasting</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">5G vertical application</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">unlabeled data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">network quality</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">neural networks</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Engineering (General). Civil engineering (General)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Biology (General)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Physics</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Chemistry</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Bin Chen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Taoye Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Kang Cao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yuming Ding</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Tianhang Gao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhongyuan Zhao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Applied Sciences</subfield><subfield code="d">MDPI AG, 2012</subfield><subfield code="g">13(2023), 10745, p 10745</subfield><subfield code="w">(DE-627)737287640</subfield><subfield code="w">(DE-600)2704225-X</subfield><subfield code="x">20763417</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:13</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:10745, p 10745</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/app131910745</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/afaa1d586a57414ea1a35cc4515b4015</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2076-3417/13/19/10745</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2076-3417</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">13</subfield><subfield code="j">2023</subfield><subfield code="e">10745, p 10745</subfield></datafield></record></collection>
|
callnumber-first |
T - Technology |
author |
Qing Zhang |
spellingShingle |
Qing Zhang misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc anomaly detection and forecasting misc 5G vertical application misc unlabeled data misc network quality misc neural networks misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry Generative Adversarial Network-Based Anomaly Detection and Forecasting with Unlabeled Data for 5G Vertical Applications |
authorStr |
Qing Zhang |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)737287640 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut aut aut aut aut |
collection |
DOAJ |
remote_str |
true |
callnumber-label |
TA1-2040 |
illustrated |
Not Illustrated |
issn |
20763417 |
topic_title |
TA1-2040 QH301-705.5 QC1-999 QD1-999 Generative Adversarial Network-Based Anomaly Detection and Forecasting with Unlabeled Data for 5G Vertical Applications anomaly detection and forecasting 5G vertical application unlabeled data network quality neural networks |
topic |
misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc anomaly detection and forecasting misc 5G vertical application misc unlabeled data misc network quality misc neural networks misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry |
topic_unstemmed |
misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc anomaly detection and forecasting misc 5G vertical application misc unlabeled data misc network quality misc neural networks misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry |
topic_browse |
misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc anomaly detection and forecasting misc 5G vertical application misc unlabeled data misc network quality misc neural networks misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Applied Sciences |
hierarchy_parent_id |
737287640 |
hierarchy_top_title |
Applied Sciences |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)737287640 (DE-600)2704225-X |
title |
Generative Adversarial Network-Based Anomaly Detection and Forecasting with Unlabeled Data for 5G Vertical Applications |
ctrlnum |
(DE-627)DOAJ093245246 (DE-599)DOAJafaa1d586a57414ea1a35cc4515b4015 |
title_full |
Generative Adversarial Network-Based Anomaly Detection and Forecasting with Unlabeled Data for 5G Vertical Applications |
author_sort |
Qing Zhang |
journal |
Applied Sciences |
journalStr |
Applied Sciences |
callnumber-first-code |
T |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2023 |
contenttype_str_mv |
txt |
author_browse |
Qing Zhang Bin Chen Taoye Zhang Kang Cao Yuming Ding Tianhang Gao Zhongyuan Zhao |
container_volume |
13 |
class |
TA1-2040 QH301-705.5 QC1-999 QD1-999 |
format_se |
Elektronische Aufsätze |
author-letter |
Qing Zhang |
doi_str_mv |
10.3390/app131910745 |
author2-role |
verfasserin |
title_sort |
generative adversarial network-based anomaly detection and forecasting with unlabeled data for 5g vertical applications |
callnumber |
TA1-2040 |
title_auth |
Generative Adversarial Network-Based Anomaly Detection and Forecasting with Unlabeled Data for 5G Vertical Applications |
abstract |
With the development of 5G vertical applications, a huge amount of unlabeled network data can be collected, which can be employed for evaluating the user experience and network operation status, such as the identifications and predictions of network anomalies. However, it is challenging to achieve highly accurate evaluation results using the conventional statistical methods due to the limitations of data quality. In this paper, generative adversarial network (GAN)-based anomaly detection and forecasting are studied for 5G vertical applications, which can provide considerable detection and prediction results with unlabeled network data samples. First, the paradigm and deployment of the deep-learning-based anomaly detection and forecasting scheme are designed. Second, the network structure and the training strategy are introduced to fully explore the potential of the GAN model. Finally, the experimental results of our proposed GAN model are provided based on the practical unlabeled network operation data in various 5G vertical scenarios, which show that our proposed scheme can achieve significant performance gains for network anomaly detection and forecasting. |
abstractGer |
With the development of 5G vertical applications, a huge amount of unlabeled network data can be collected, which can be employed for evaluating the user experience and network operation status, such as the identifications and predictions of network anomalies. However, it is challenging to achieve highly accurate evaluation results using the conventional statistical methods due to the limitations of data quality. In this paper, generative adversarial network (GAN)-based anomaly detection and forecasting are studied for 5G vertical applications, which can provide considerable detection and prediction results with unlabeled network data samples. First, the paradigm and deployment of the deep-learning-based anomaly detection and forecasting scheme are designed. Second, the network structure and the training strategy are introduced to fully explore the potential of the GAN model. Finally, the experimental results of our proposed GAN model are provided based on the practical unlabeled network operation data in various 5G vertical scenarios, which show that our proposed scheme can achieve significant performance gains for network anomaly detection and forecasting. |
abstract_unstemmed |
With the development of 5G vertical applications, a huge amount of unlabeled network data can be collected, which can be employed for evaluating the user experience and network operation status, such as the identifications and predictions of network anomalies. However, it is challenging to achieve highly accurate evaluation results using the conventional statistical methods due to the limitations of data quality. In this paper, generative adversarial network (GAN)-based anomaly detection and forecasting are studied for 5G vertical applications, which can provide considerable detection and prediction results with unlabeled network data samples. First, the paradigm and deployment of the deep-learning-based anomaly detection and forecasting scheme are designed. Second, the network structure and the training strategy are introduced to fully explore the potential of the GAN model. Finally, the experimental results of our proposed GAN model are provided based on the practical unlabeled network operation data in various 5G vertical scenarios, which show that our proposed scheme can achieve significant performance gains for network anomaly detection and forecasting. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 |
container_issue |
10745, p 10745 |
title_short |
Generative Adversarial Network-Based Anomaly Detection and Forecasting with Unlabeled Data for 5G Vertical Applications |
url |
https://doi.org/10.3390/app131910745 https://doaj.org/article/afaa1d586a57414ea1a35cc4515b4015 https://www.mdpi.com/2076-3417/13/19/10745 https://doaj.org/toc/2076-3417 |
remote_bool |
true |
author2 |
Bin Chen Taoye Zhang Kang Cao Yuming Ding Tianhang Gao Zhongyuan Zhao |
author2Str |
Bin Chen Taoye Zhang Kang Cao Yuming Ding Tianhang Gao Zhongyuan Zhao |
ppnlink |
737287640 |
callnumber-subject |
TA - General and Civil Engineering |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.3390/app131910745 |
callnumber-a |
TA1-2040 |
up_date |
2024-07-03T16:08:54.768Z |
_version_ |
1803574769014013952 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ093245246</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413160918.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240413s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/app131910745</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ093245246</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJafaa1d586a57414ea1a35cc4515b4015</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TA1-2040</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QH301-705.5</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QC1-999</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QD1-999</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Qing Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Generative Adversarial Network-Based Anomaly Detection and Forecasting with Unlabeled Data for 5G Vertical Applications</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">With the development of 5G vertical applications, a huge amount of unlabeled network data can be collected, which can be employed for evaluating the user experience and network operation status, such as the identifications and predictions of network anomalies. However, it is challenging to achieve highly accurate evaluation results using the conventional statistical methods due to the limitations of data quality. In this paper, generative adversarial network (GAN)-based anomaly detection and forecasting are studied for 5G vertical applications, which can provide considerable detection and prediction results with unlabeled network data samples. First, the paradigm and deployment of the deep-learning-based anomaly detection and forecasting scheme are designed. Second, the network structure and the training strategy are introduced to fully explore the potential of the GAN model. Finally, the experimental results of our proposed GAN model are provided based on the practical unlabeled network operation data in various 5G vertical scenarios, which show that our proposed scheme can achieve significant performance gains for network anomaly detection and forecasting.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">anomaly detection and forecasting</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">5G vertical application</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">unlabeled data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">network quality</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">neural networks</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Engineering (General). Civil engineering (General)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Biology (General)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Physics</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Chemistry</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Bin Chen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Taoye Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Kang Cao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yuming Ding</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Tianhang Gao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhongyuan Zhao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Applied Sciences</subfield><subfield code="d">MDPI AG, 2012</subfield><subfield code="g">13(2023), 10745, p 10745</subfield><subfield code="w">(DE-627)737287640</subfield><subfield code="w">(DE-600)2704225-X</subfield><subfield code="x">20763417</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:13</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:10745, p 10745</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/app131910745</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/afaa1d586a57414ea1a35cc4515b4015</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2076-3417/13/19/10745</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2076-3417</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">13</subfield><subfield code="j">2023</subfield><subfield code="e">10745, p 10745</subfield></datafield></record></collection>
|
score |
7.400321 |