{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T09:33:21Z","timestamp":1763458401446,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":10,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T00:00:00Z","timestamp":1718928000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,6,21]]},"DOI":"10.1145\/3690407.3690426","type":"proceedings-article","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T18:55:28Z","timestamp":1729796128000},"page":"113-118","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Network Intrusion Detection Based on Balanced Data"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1293-8336","authenticated-orcid":false,"given":"Xuecheng","family":"Yu","sequence":"first","affiliation":[{"name":"Xinjiang University, Urumqi, Xinjiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6671-0206","authenticated-orcid":false,"given":"Zhenhong","family":"Jia","sequence":"additional","affiliation":[{"name":"The Autonomous University Key Laboratory of signal and Information Processing Laboratory, Xinjiang University, Urumqi, Xinjiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1236-2060","authenticated-orcid":false,"given":"Xiaohui","family":"Huang","sequence":"additional","affiliation":[{"name":"Xinjiang University, Urumqi, Xinjiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1495-8610","authenticated-orcid":false,"given":"Sensen","family":"Song","sequence":"additional","affiliation":[{"name":"Xinjiang University, Urumqi, Xinjiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0219-4051","authenticated-orcid":false,"given":"Jiajia","family":"Wang","sequence":"additional","affiliation":[{"name":"Xinjiang University, Urumqi, Xinjiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8390-5510","authenticated-orcid":false,"given":"Gang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Xinjiang University, Urumqi, Xinjiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1144-6564","authenticated-orcid":false,"given":"Fei","family":"Shi","sequence":"additional","affiliation":[{"name":"Xinjiang University, Urumqi, Xinjiang, China"}]}],"member":"320","published-online":{"date-parts":[[2024,10,24]]},"reference":[{"key":"e_1_3_3_1_1_2","doi-asserted-by":"publisher","DOI":"10.3390\/app13042576"},{"key":"e_1_3_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3130234"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"crossref","unstructured":"Pioro\u0144ski S G\u00f3recki T. Using GAN to Generate Malicious Samples Suitable for Binary Classifier Training[C]\/\/2022 IEEE International Conference on Big Data. 2022: 6522-6527.","DOI":"10.1109\/BigData55660.2022.10020840"},{"key":"e_1_3_3_1_4_2","article-title":"A fuzzy rough set-based undersampling approach for imbalanced data","author":"Zhang","year":"2024","unstructured":"Zhang, Xiao, et al. \"A fuzzy rough set-based undersampling approach for imbalanced data\". International Journal of Machine Learning and Cybernetics, 2024.","journal-title":"International Journal of Machine Learning and Cybernetics"},{"key":"e_1_3_3_1_5_2","volume-title":"Advances in Data Analysis and Classification","author":"Sadhukhan","year":"2024","unstructured":"Sadhukhan, Payel, and Palit Sarbani. \"Natural-neighborhood based, label-specific undersampling for imbalanced, multi-label data\". Advances in Data Analysis and Classification, 2024."},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121053"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Yang X Q Wang L. SDP-CycleGAN: CycleGAN Based on Siamese Data Pairs for Unraveling Data Imbalance Problems in Anomaly Detection[C]\/\/2021 Ninth International Conference on Advanced Cloud and Big Data. 2022: 151-157.","DOI":"10.1109\/CBD54617.2021.00034"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121053"},{"key":"e_1_3_3_1_9_2","volume-title":"A Network Intrusion Detection Model Based on BiLSTM with Multi-Head Attention Mechanism. Electronics","author":"Zhang J.","year":"2023","unstructured":"Zhang, J.; Zhang, X.; Liu, Z.; Fu, F.; Jiao, Y.; Xu, F. A Network Intrusion Detection Model Based on BiLSTM with Multi-Head Attention Mechanism. Electronics 2023, 12, 4170."},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2023.044999"}],"event":{"name":"CAIBDA 2024: 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms","acronym":"CAIBDA 2024","location":"Zhengzhou China"},"container-title":["Proceedings of the 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690407.3690426","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3690407.3690426","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:09:37Z","timestamp":1750295377000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690407.3690426"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,21]]},"references-count":10,"alternative-id":["10.1145\/3690407.3690426","10.1145\/3690407"],"URL":"https:\/\/doi.org\/10.1145\/3690407.3690426","relation":{},"subject":[],"published":{"date-parts":[[2024,6,21]]},"assertion":[{"value":"2024-10-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}