{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T19:00:48Z","timestamp":1785006048208,"version":"3.55.0"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61961020"],"award-info":[{"award-number":["61961020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s10586-026-06163-0","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T11:02:14Z","timestamp":1781694134000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Network intrusion detection method based on CAE-TCGAN combined with feature extraction"],"prefix":"10.1007","volume":"29","author":[{"given":"Wenting","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhua","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Gong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,17]]},"reference":[{"key":"6163_CR1","unstructured":"China Internet Network Information Center (CNNIC). The 56th Statistical Report on China\u2019s Internet Development. China Internet Network Information Center. Accessed 14 April 2025. Beijing, China, July 2025. https:\/\/www.cnnic.net.cn\/hlwfzyj\/hlwxzbg\/hlwtjbg\/202507\/t20250721_72492.htm"},{"issue":"5","key":"6163_CR2","doi-asserted-by":"publisher","DOI":"10.3390\/fi17050216","volume":"17","author":"J Li","year":"2025","unstructured":"Li, J., et al.: Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs. Future Internet 17(5), 216 (2025). https:\/\/doi.org\/10.3390\/fi17050216","journal-title":"Future Internet"},{"key":"6163_CR3","unstructured":"Goodfellow, I.J.: Generative Adversarial Nets. In: Ghahramani, Z. (ed.) Advances in Neural Information Processing Systems, vol. 27, pp. 2674\u20132682. Curran Associates, Inc., Montreal, Canada (2014)"},{"key":"6163_CR4","doi-asserted-by":"crossref","unstructured":"Mubarakali, A.: \u201cNovel GAN-based privacy-enhanced intrusion detection system for cyberattack classification\u201d. In: Intelligent Data Analysis: An International Journal (2025). https:\/\/api.semanticscholar.org\/CorpusID:282164454","DOI":"10.1177\/1088467X251372743"},{"issue":"3","key":"6163_CR5","doi-asserted-by":"publisher","first-page":"3899","DOI":"10.32604\/cmes.2025.064874","volume":"143","author":"MS Alshehri","year":"2025","unstructured":"Alshehri, M.S., et al.: \u201cA Hybrid Wasserstein GAN and Autoencoder Model for Robust Intrusion Detection in IoT.\u201d Computer Modeling in Engineering & Sciences 143(3), 3899\u20133920 (2025). https:\/\/doi.org\/10.32604\/cmes.2025.064874","journal-title":"Computer Modeling in Engineering & Sciences"},{"key":"6163_CR6","doi-asserted-by":"publisher","DOI":"10.3390\/fi17060258","volume":"17","author":"PS Moghaddam","year":"2025","unstructured":"Moghaddam, P.S., et al.: \u201cGenerative Adversarial and Transformer Network Synergy for Robust Intrusion Detection in IoT Environments.\u201d Future Internet 17, 258 (2025)","journal-title":"Future Internet"},{"key":"6163_CR7","doi-asserted-by":"publisher","DOI":"10.3390\/app15095002","author":"M Basak","year":"2025","unstructured":"Basak, M., et al.: \u201cX-GANet: An Explainable Graph-Based Framework for Robust Network Intrusion Detection.\u201d Applied Sciences (2025). https:\/\/doi.org\/10.3390\/app15095002","journal-title":"Applied Sciences"},{"key":"6163_CR8","doi-asserted-by":"publisher","DOI":"10.3390\/electronics15061225","author":"K Fu","year":"2026","unstructured":"Fu, K., et al.: \u201cWASAE-NIDS: Reverse-Frequency Class Weighting with GAN-Assisted Conditional Autoencoder for Network Intrusion Detection.\u201d Electronics (2026). https:\/\/doi.org\/10.3390\/electronics15061225","journal-title":"Electronics"},{"issue":"2","key":"6163_CR9","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1109\/TR.2022.3204349","volume":"72","author":"Z Li","year":"2023","unstructured":"Li, Z., et al.: Abnormal Traffic Detection: Traffic Feature Extraction and DAE-GAN With Efficient Data Augmentation. IEEE Transactions on Reliability 72(2), 498\u2013510 (2023). https:\/\/doi.org\/10.1109\/TR.2022.3204349","journal-title":"IEEE Transactions on Reliability"},{"key":"6163_CR10","doi-asserted-by":"publisher","unstructured":"Sabeel, U., et al.: \u201cCVAE-AN: Atypical Attack Flow Detection Using Incremental Adversarial Learning\u201d. In: 2021 IEEE Global Communications Conference (GLOBECOM). 2021, pp.1\u20136. https:\/\/doi.org\/10.1109\/GLOBECOM46510.2021.9685699","DOI":"10.1109\/GLOBECOM46510.2021.9685699"},{"key":"6163_CR11","doi-asserted-by":"publisher","unstructured":"Tavallaee, M., et al.: \u201cA detailed analysis of the KDD CUP 99 data set\u201d. In: 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications. (2009), pp.1\u20136. https:\/\/doi.org\/10.1109\/CISDA.2009.5356528","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"6163_CR12","doi-asserted-by":"publisher","unstructured":"Sinha, J., Manollas, M.: \u201cEfficient Deep CNN-BiLSTM Model for Network Intrusion Detection\u201d. In: Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition. AIPR \u201920. Xiamen, China: Association for Computing Machinery, (2020), 223\u2013231. ISSN: 9781450375511. https:\/\/doi.org\/10.1145\/3430199.3430224","DOI":"10.1145\/3430199.3430224"},{"key":"6163_CR13","unstructured":"Mirza, M., Osindero, S.: Conditional Generative Adversarial Nets. (2014). arXiv: https:\/\/arxiv.org\/abs\/1411.1784"},{"key":"6163_CR14","doi-asserted-by":"crossref","unstructured":"Masci, J., et al.: \u201cStacked Convolutional Auto-Encoders for Hierarchical Feature Extraction\u201d. In: Artificial Neural Networks and Machine Learning \u2013 ICANN 2011. Ed. by Timo Honkela et al. Berlin, Heidelberg: Springer Berlin Heidelberg, (2011), pp.52\u201359. ISSN: 978-3-642-21735-7","DOI":"10.1007\/978-3-642-21735-7_7"},{"issue":"6088","key":"6163_CR15","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. nature 323(6088), 533\u2013536 (1986)","journal-title":"nature"},{"issue":"1","key":"6163_CR16","doi-asserted-by":"publisher","first-page":"162","DOI":"10.21629\/JSEE.2017.01.18","volume":"28","author":"B Zhao","year":"2017","unstructured":"Zhao, B., et al.: Convolutional neural networks for time series classification. Journal of Systems Engineering and Electronics 28(1), 162\u2013169 (2017). https:\/\/doi.org\/10.21629\/JSEE.2017.01.18","journal-title":"Journal of Systems Engineering and Electronics"},{"issue":"8","key":"6163_CR17","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long Short-Term Memory. Neural Comput. 9(8), 1735\u20131780 (1997). https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput."},{"key":"6163_CR18","doi-asserted-by":"publisher","DOI":"10.3390\/computers14070291","author":"T Wisanwanichthan","year":"2025","unstructured":"Wisanwanichthan, T., Thammawichai, M.: A Lightweight Intrusion Detection System for IoT and UAV Using Deep Neural Networks with Knowledge Distillation. Computers (2025). https:\/\/doi.org\/10.3390\/computers14070291","journal-title":"Computers"},{"key":"6163_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2024.103926","volume":"144","author":"LD Rose","year":"2024","unstructured":"Rose, L.D., et al.: VINCENT: Cyber-threat detection through vision transformers and knowledge distillation. Computers & Security 144, 103926 (2024). https:\/\/doi.org\/10.1016\/j.cose.2024.103926","journal-title":"Computers & Security"},{"key":"6163_CR20","doi-asserted-by":"publisher","first-page":"190431","DOI":"10.1109\/ACCESS.2020.3031892","volume":"8","author":"G Zhang","year":"2020","unstructured":"Zhang, G., et al.: Network Intrusion Detection Based on Conditional Wasserstein Generative Adversarial Network and Cost-Sensitive Stacked Autoencoder. IEEE Access 8, 190431\u2013190447 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3031892","journal-title":"IEEE Access"},{"issue":"3","key":"6163_CR21","doi-asserted-by":"publisher","first-page":"2330","DOI":"10.1109\/JIOT.2022.3211346","volume":"10","author":"C Park","year":"2023","unstructured":"Park, C., et al.: An Enhanced AI-Based Network Intrusion Detection System Using Generative Adversarial Networks. IEEE Internet of Things Journal 10(3), 2330\u20132345 (2023). https:\/\/doi.org\/10.1109\/JIOT.2022.3211346","journal-title":"IEEE Internet of Things Journal"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-026-06163-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-026-06163-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-026-06163-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T18:31:38Z","timestamp":1785004298000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-026-06163-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":21,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["6163"],"URL":"https:\/\/doi.org\/10.1007\/s10586-026-06163-0","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"30 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 April 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"378"}}