{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T18:40:05Z","timestamp":1755974405458,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T00:00:00Z","timestamp":1716940800000},"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":[[2024,5,29]]},"DOI":"10.1145\/3686540.3686543","type":"proceedings-article","created":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T12:23:04Z","timestamp":1730982184000},"page":"17-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Optimisation of Machine Learning Based Data Mining Methods for Network Intrusion Detection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3502-2953","authenticated-orcid":false,"given":"Mingxiao","family":"Li","sequence":"first","affiliation":[{"name":"Qinghai Minzu University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5926-8537","authenticated-orcid":false,"given":"Ziqing","family":"Li","sequence":"additional","affiliation":[{"name":"Qinghai Minzu University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5832-7876","authenticated-orcid":false,"given":"Chenlong","family":"Liu","sequence":"additional","affiliation":[{"name":"Qinghai Minzu University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4445-106X","authenticated-orcid":false,"given":"Wanqi","family":"Chen","sequence":"additional","affiliation":[{"name":"Qinghai Minzu University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5696-5759","authenticated-orcid":false,"given":"Chaojie","family":"Ma","sequence":"additional","affiliation":[{"name":"Qinghai Minzu University, China"}]}],"member":"320","published-online":{"date-parts":[[2024,9,19]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"651","volume-title":"2012 international conference on computer science and electronics engineering","volume":"1","author":"Chen Deyan","unstructured":"Deyan Chen and Hong Zhao. Data security and privacy protection issues in cloud computing. In 2012 international conference on computer science and electronics engineering, volume 1, pages 647\u2013651. IEEE, 2012."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_2_1","DOI":"10.7763\/IJCTE.2009.V1.87"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_3_1","DOI":"10.1007\/s41870-020-00427-7"},{"key":"e_1_3_2_1_4_1","volume-title":"Smote for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. Journal of artificial intelligence research, 61:863\u2013905","author":"Fern\u00e1ndez Alberto","year":"2018","unstructured":"Alberto Fern\u00e1ndez, Salvador Garcia, Francisco Herrera, and Nitesh V Chawla. Smote for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. Journal of artificial intelligence research, 61:863\u2013905, 2018."},{"key":"e_1_3_2_1_5_1","article-title":"Fusing deep learning and smote for imbalanced data","author":"Dablain Damien","year":"2022","unstructured":"Damien Dablain, Bartosz Krawczyk, and Nitesh V Chawla. Deepsmote: Fusing deep learning and smote for imbalanced data. IEEE Transactions on Neural Networks and Learning Systems, 2022.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_1_6_1","first-page":"18","volume-title":"Complex & Intelligent Systems","author":"Leng Qiangkui","year":"2024","unstructured":"Qiangkui Leng, Jiamei Guo, Jiaqing Tao, Xiangfu Meng, and Changzhong Wang. Obmi: oversampling borderline minority instances by a two-stage tomek linkfinding procedure for class imbalance problem. Complex & Intelligent Systems, pages 1\u201318, 2024."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_7_1","DOI":"10.7717\/peerj-cs.1775"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_8_1","DOI":"10.7763\/IJCTE.2013.V5.675"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_9_1","DOI":"10.1016\/j.eswa.2020.114060"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_10_1","DOI":"10.7763\/IJCTE.2013.V5.753"},{"key":"e_1_3_2_1_11_1","volume-title":"Jos\u00e9 Manuel Ben\u00edtez, and Francisco Herrera. Big data preprocessing: methods and prospects. Big data analytics, 1:1\u201322","author":"Garc\u00eda Salvador","year":"2016","unstructured":"Salvador Garc\u00eda, Sergio Ram\u00edrez-Gallego, Juli\u00e1n Luengo, Jos\u00e9 Manuel Ben\u00edtez, and Francisco Herrera. Big data preprocessing: methods and prospects. Big data analytics, 1:1\u201322, 2016."},{"key":"e_1_3_2_1_12_1","article-title":"A detailed analysis of cicids2017 dataset for designing intrusion detection systems","volume":"479","author":"Panigrahi Ranjit","year":"2018","unstructured":"Ranjit Panigrahi and Samarjeet Borah. A detailed analysis of cicids2017 dataset for designing intrusion detection systems. International Journal of Engineering & Technology, 7(3.24):479\u2013482, 2018.","journal-title":"International Journal of Engineering & Technology, 7(3.24)"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_13_1","DOI":"10.1145\/2882903.2912574"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_14_1","DOI":"10.1109\/TNN.2008.2005601"},{"key":"e_1_3_2_1_15_1","volume-title":"Feature engineering for machine learning: principles and techniques for data scientists. \" O'Reilly Media","author":"Zheng Alice","year":"2018","unstructured":"Alice Zheng and Amanda Casari. Feature engineering for machine learning: principles and techniques for data scientists. \" O'Reilly Media, Inc.\", 2018."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_16_1","DOI":"10.1016\/j.patcog.2019.02.023"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_17_1","DOI":"10.1186\/s12957-023-03108-4"},{"doi-asserted-by":"crossref","unstructured":"Farkhanda Abbas Zhihua Cai Muhammad Shoaib Javed Iqbal Muhammad Ismail ARIF ULLAH Abdulwahed Fahad Alrefaei and Mohammed Fahad Albeshr. Uncertainty analysis of predictive models for water quality index: Comparative analysis of xgboost random forest svm knn gradient boosting and decision tree algorithms. 2024.","key":"e_1_3_2_1_18_1","DOI":"10.20944\/preprints202402.0828.v1"},{"issue":"125","key":"e_1_3_2_1_19_1","first-page":"1","article-title":"Classification with deep neural networks and logistic loss","volume":"25","author":"Zhang Zihan","year":"2024","unstructured":"Zihan Zhang, Lei Shi, and Ding-Xuan Zhou. Classification with deep neural networks and logistic loss. Journal of Machine Learning Research, 25(125):1\u2013117, 2024.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_20_1","first-page":"012018","volume-title":"Journal of Physics: Conference Series","volume":"1192","author":"Yulianto Arif","unstructured":"Arif Yulianto, Parman Sukarno, and Novian Anggis Suwastika. Improving adaboost-based intrusion detection system (ids) performance on cic ids 2017 dataset. In Journal of Physics: Conference Series, volume 1192, page 012018. IOP Publishing, 2019."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_21_1","DOI":"10.3390\/electronics8030322"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_22_1","DOI":"10.1016\/j.adhoc.2020.102177"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_23_1","DOI":"10.1016\/j.future.2022.01.026"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_24_1","DOI":"10.1007\/s00521-020-05500-7"}],"event":{"acronym":"BDSIC 2024","name":"BDSIC 2024: 2024 6th International Conference on Big-data Service and Intelligent Computation","location":"Hong Kong Hong Kong"},"container-title":["Proceedings of the 2024 6th International Conference on Big-data Service and Intelligent Computation"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3686540.3686543","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3686540.3686543","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T18:21:49Z","timestamp":1755973309000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3686540.3686543"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,29]]},"references-count":24,"alternative-id":["10.1145\/3686540.3686543","10.1145\/3686540"],"URL":"https:\/\/doi.org\/10.1145\/3686540.3686543","relation":{},"subject":[],"published":{"date-parts":[[2024,5,29]]},"assertion":[{"value":"2024-09-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}