{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:52:42Z","timestamp":1777510362787,"version":"3.51.4"},"reference-count":23,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2023,7,1]]},"DOI":"10.1587\/transinf.2022edp7187","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T22:19:04Z","timestamp":1688163544000},"page":"1209-1218","source":"Crossref","is-referenced-by-count":10,"title":["GAN-SR Anomaly Detection Model Based on Imbalanced Data"],"prefix":"10.1587","volume":"E106.D","author":[{"given":"Shuang","family":"WANG","sequence":"first","affiliation":[{"name":"Evaluation Center of Civil Aviation University of China"}]},{"given":"Hui","family":"CHEN","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Civil Aviation University of China"}]},{"given":"Lei","family":"DING","sequence":"additional","affiliation":[{"name":"School of Cyberspaces Security, Guangzhou University"}]},{"given":"He","family":"SUI","sequence":"additional","affiliation":[{"name":"Aeronautical Engineering Institute, Civil Aviation University of China"}]},{"given":"Jianli","family":"DING","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Civil Aviation University of China"}]}],"member":"532","reference":[{"key":"1","unstructured":"[1] Z. Gu, C. Cai, and M. Wang, \u201cThe masking method of civil aviation passenger data based on improved format-preserving encryption,\u201d Netinfo Security (in Chinese), vol.21, no.5, pp.39-47, 2021. 10.3969\/j.issn.1671-1122.2021.05.005"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] X. Liu, T. Li, R. Zhang, D. Wu, Y. Liu, and Z. Yang, \u201cA gan and feature selection-based oversampling technique for intrusion detection,\u201d Security and Communication Networks, vol.2021, pp.1-15, 2021. 10.1155\/2021\/9947059","DOI":"10.1155\/2021\/9947059"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] C. Dylan and J. Meng, \u201cA survey on data-driven network intrusion detection,\u201d ACM Computing Surveys (CSUR), vol.54, no.9, pp.1-36, 2021. 10.1145\/3472753","DOI":"10.1145\/3472753"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] L. Zhang, Y. Cui, J. Liu, Y. Jiang, and J.P. Wu, \u201cApplication of machine learning in cyberspace security research,\u201d Chinese Journal of Computers, 2018.","DOI":"10.2991\/cimns-18.2018.9"},{"key":"5","unstructured":"[5] J.-Y. Zhou, P.-F. He, R.-F. Qiu, G. Chen, and W.-G. Wu, \u201cResearch on intrusion detection based on random forest and gradient boosting tree,\u201d Journal of Software, vol.32, no.10, pp.3254-3265, 2021. 10.13328\/j.cnki.jos.006062"},{"key":"6","unstructured":"[6] H. Xu, Z. Ma, H. Yi, and L. Zhang, \u201cNetwork traffic anomaly detection technology based on convolutional recurrent neural network,\u201d Netinfo Security (in Chinese), vol.21, no.7, pp.54-62, 2021. 10.3969\/j.issn.1671-1122.2021.07.007"},{"key":"7","unstructured":"[7] S. Dong and B. Zhang, \u201cNetwork traffic anomaly detection method based on deep features learning,\u201d Journal of Electronics and Information Technology, vol.42, no.3, pp.695-703, 2020."},{"key":"8","unstructured":"[8] R. Jiadong, Z. Yafei, Z. Bing, and L. Shangyang, \u201cClassification method of industrial internet intrusion detedtion based on feature selection,\u201d Journal of Computer Research and Development, 005, 059, 2022."},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] G. Pang, C. Shen, L. Cao, and V. Anton, \u201cDeep learning for anomaly detection: A review,\u201d ACM Computing Surveys, vol.54, no.2, pp.1-38, 2022. 10.1145\/3439950","DOI":"10.1145\/3439950"},{"key":"10","unstructured":"[10] H. Zenati, C.S. Foo, B. Lecouat, G. Manek, and V.R. Chandrasekhar, \u201cEfficient gan-based anomaly detection,\u201d arXiv preprint arXiv:1802.06222, 2018."},{"key":"11","unstructured":"[11] Z. Gu, T. Liu, and H. Sui, \u201cLatent feature reconstruction generative gan model for ics anomaly detection,\u201d Journal of Xidian Universitn (in Chinese), vol.49, no.2, pp.173-181, 2022. 10.19665\/j.issn1001-2400.2022.02.020"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] I.F. Kilincer, F. Ertam, and A. Sengur, \u201cMachine learning methods for cyber security intrusion detection: Datasets and comparative study,\u201d Computer Networks, vol.188, p.107840, 2021. 10.1016\/j.comnet.2021.107840","DOI":"10.1016\/j.comnet.2021.107840"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] J. Zhang, Y. Ling, X. Fu, X. Yang, G. Xiong, and R. Zhang, \u201cModel of the intrusion detection system based on the integration of spatial-temporal features,\u201d Computers & Security, vol.89, p.101681, 2020. 10.1016\/j.cose.2019.101681","DOI":"10.1016\/j.cose.2019.101681"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] F. Gottwalt, E. Chang, and T. Dillon, \u201cCorrcorr: A feature selection method formultivariate correlation network anomaly detection techniques,\u201d Computers& Security, vol.83, pp.234-245, 2019. 10.1016\/j.cose.2019.02.008","DOI":"10.1016\/j.cose.2019.02.008"},{"key":"15","unstructured":"[15] Y. Song, B. Hou, and Z. Cai, \u201cNetwork intrusion detection method based on deep learning feature extraction,\u201d Journal of Huazhong University of science and Technology (NATURAL SCIENCE EDITION), vol.49, no.2, pp.115-120, 2021. 10.13245\/j.hust.210219"},{"key":"16","unstructured":"[16] L. An, Z. Han, S. Lin, and W. Shang, \u201cResearch on gan-sdae-rf model for network intrusion detection,\u201d Computer Engineering and Applications (in Chinese), vol.57, no.21, pp.155-164, 2021. 10.3778\/j.issn.1002-8331.2007-0264"},{"key":"17","unstructured":"[17] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, \u201cGenerative adversarial nets,\u201d Proceedings of the 27th International Conference on Neural Information Processing Systems, vol.2, pp.2672-2680, 2014."},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] N. Shone, T.N. Ngoc, V.D. Phai, and Q. Shi, \u201cA deep learning approach to network intrusion detection,\u201d IEEE transactions on emerging topics in computational intelligence, vol.2, no.1, pp.41-50, 2018. 10.1109\/tetci.2017.2772792","DOI":"10.1109\/TETCI.2017.2772792"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] Z. Gu, L. Wang, C. Liu, and Z. Wang, \u201cNetwork intrusion detection with nonsymmetric deep autoencoding feature extraction,\u201d Security and Communication Networks, vol.2021, pp.1-11, 2021. 10.1155\/2021\/2843856","DOI":"10.1155\/2021\/2843856"},{"key":"20","unstructured":"[20] N. Moustafa and J. Slay, \u201cThe unsw-nb15 data set description,\u201d Unsw. adfa.edu. au. [Online]https:\/\/www.unsw.adfa.edu.au\/unsw-canberracyber\/cybersecurity\/ADFANB15-Datasets, 2016."},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] M. Sarhan, S. Layeghy, N. Moustafa, and M. Portmann, \u201cNetflow datasets for machine learning-based network intrusion detection systems,\u201d Big Data Technologies and Applications: 10th EAI International Conference, BDTA 2020, and 13th EAI International Conference on Wireless Internet, WiCON 2020, Virtual Event, Dec. 11, 2020, Proceedings 10, Springer, vol.371, pp.117-135, 2021. 10.1007\/978-3-030-72802-1_9","DOI":"10.1007\/978-3-030-72802-1_9"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] A.P. Mathur and N.O. Tippenhauer, \u201cSwat: a water treatment testbed for research and training on ics security,\u201d 2016 International Workshop on Cyber-physical Systems for Smart Water Networks (CySWater), pp.31-36, 2016. doi:10.1109\/CySWater.2016.7469060 10.1109\/cyswater.2016.7469060","DOI":"10.1109\/CySWater.2016.7469060"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] S. Kriaa, L. Pietre-Cambacedes, M. Bouissou, and Y. Halgand, \u201cA survey of approaches combining safety and security for industrial control systems,\u201d Reliability engineering & system safety, vol.139, pp.156-178, 2015. 10.1016\/j.ress.2015.02.008","DOI":"10.1016\/j.ress.2015.02.008"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E106.D\/7\/E106.D_2022EDP7187\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T04:22:26Z","timestamp":1688185346000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E106.D\/7\/E106.D_2022EDP7187\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,1]]},"references-count":23,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2022edp7187","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,1]]},"article-number":"2022EDP7187"}}