{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T20:32:35Z","timestamp":1791664355011,"version":"4.3.4"},"reference-count":31,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,9]],"date-time":"2022-07-09T00:00:00Z","timestamp":1657324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61701170"],"award-info":[{"award-number":["61701170"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["222102210052"],"award-info":[{"award-number":["222102210052"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["222102210007"],"award-info":[{"award-number":["222102210007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["222102210062"],"award-info":[{"award-number":["222102210062"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["222102210272"],"award-info":[{"award-number":["222102210272"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Special project for key R&amp;D and promotion of Henan Province","award":["61701170"],"award-info":[{"award-number":["61701170"]}]},{"name":"Special project for key R&amp;D and promotion of Henan Province","award":["222102210052"],"award-info":[{"award-number":["222102210052"]}]},{"name":"Special project for key R&amp;D and promotion of Henan Province","award":["222102210007"],"award-info":[{"award-number":["222102210007"]}]},{"name":"Special project for key R&amp;D and promotion of Henan Province","award":["222102210062"],"award-info":[{"award-number":["222102210062"]}]},{"name":"Special project for key R&amp;D and promotion of Henan Province","award":["222102210272"],"award-info":[{"award-number":["222102210272"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Network attack traffic detection plays a crucial role in protecting network operations and services. To accurately detect malicious traffic on the internet, this paper designs a hybrid algorithm UMAP-RF for both binary and multiclassification network attack detection tasks. First, the network traffic data are dimensioned down with UMAP algorithm. The random forest algorithm is improved based on parameter optimization, and the improved random forest algorithm is used to classify the network traffic data, distinguishing normal data from abnormal data and classifying nine different types of network attacks from the abnormal data. Experimental results on the UNSW-NB15 dataset, which are significant improvements compared to traditional machine-learning methods, show that the UMAP-RF hybrid model can perform network attack traffic detection effectively, with accuracy and recall rates of 92.6% and 91%, respectively.<\/jats:p>","DOI":"10.3390\/a15070238","type":"journal-article","created":{"date-parts":[[2022,7,10]],"date-time":"2022-07-10T21:19:28Z","timestamp":1657487968000},"page":"238","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7314-7642","authenticated-orcid":false,"given":"Xiaoyu","family":"Du","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Engineering Research Center of Spatial Information Processing, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6776-8183","authenticated-orcid":false,"given":"Cheng","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijie","family":"Han","sequence":"additional","affiliation":[{"name":"School of Software, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,9]]},"reference":[{"key":"ref_1","unstructured":"WEF (2019, January 15). The Global Risks Report 2019. Available online: https:\/\/www.weforum.org\/reports\/the-global-risks-report-2019."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Patil, S., Varadarajan, V., Walimbe, D., Gulechha, S., Shenoy, S., Raina, A., and Kotecha, K. (2021). Improving the Robustness of AI-Based Malware Detection Using Adversarial Machine Learning. Algorithms, 14.","DOI":"10.3390\/a14100297"},{"key":"ref_3","unstructured":"Kim, M.S., Kong, H.J., Chung, S., and Hong, S.C. (2004, January 23). A flow-based method for abnormal network traffic detection. Proceedings of the 2004 IEEE\/IFIP Network Operations and Management Symposium (IEEE Cat. No. 04CH37507), Seoul, South Korea."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Livieris, I.E. (2019). Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks. Algorithms, 12.","DOI":"10.3390\/a12040085"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., and Melville, J. (2018). Umap: Uniform manifold approximation and projection for dimension reduction. arXiv.","DOI":"10.21105\/joss.00861"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bagui, S., Walauskis, M., DeRush, R., Praviset, H., and Boucugnani, S. (2022). Spark Configurations to Optimize Decision Tree Classification on UNSW-NB15. Big Data Cogn. Comput., 6.","DOI":"10.3390\/bdcc6020038"},{"key":"ref_7","first-page":"211","article-title":"Computational method to prove efficacy of datasets","volume":"42","author":"Singh","year":"2021","journal-title":"J. Inf. Optim. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bourou, S., El Saer, A., Velivassaki, T.-H., Voulkidis, A., and Zahariadis, T. (2021). A Review of Tabular Data Synthesis Using GANs on an IDS Dataset. Information, 12.","DOI":"10.3390\/info12090375"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Selva, D., Nagaraj, B., Pelusi, D., Arunkumar, R., and Nair, A. (2021). Intelligent Network Intrusion Prevention Feature Collection and Classification Algorithms. Algorithms, 14.","DOI":"10.3390\/a14080224"},{"key":"ref_10","first-page":"829","article-title":"Results of the DARPA 1998 Offline Intrusion Detection Evaluation","volume":"99","author":"Lippmann","year":"1999","journal-title":"Recent Adv. Intrusion Detect."},{"key":"ref_11","unstructured":"Lee, W., and Stolfo, S. (1998, January 26\u201329). Data Mining Approaches for Intrusion Detection. Proceedings of the 7th USENIX Security Symposium, San Antonio, TS, USA."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wu, D., Zhang, W., Jia, H., and Leng, X. (2021). Simultaneous Feature Selection and Support Vector Machine Optimization Using an Enhanced Chimp Optimization Algorithm. Algorithms, 14.","DOI":"10.3390\/a14100282"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1109\/21.97458","article-title":"A survey of decision tree classifier methodology","volume":"21","author":"Safavian","year":"1991","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ruggieri, A., Stranieri, F., Stella, F., and Scutari, M. (2020). Hard and Soft EM in Bayesian Network Learning from Incomplete Data. Algorithms, 13.","DOI":"10.3390\/a13120329"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"121082","DOI":"10.1016\/j.conbuildmat.2020.121082","article-title":"Prediction of chloride diffusivity in concrete using artificial neural network: Modelling and performance evaluation","volume":"268","author":"Liu","year":"2021","journal-title":"Constr. Build. Mater."},{"key":"ref_16","first-page":"225","article-title":"Research on intrusion detection model based on multiple feature selection strategies","volume":"7","author":"Hongyan","year":"2021","journal-title":"Inf. Secur. Res."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_18","first-page":"62","article-title":"Unbalanced sample flow detection based on variational autoencoder","volume":"48","author":"Renjie","year":"2021","journal-title":"Comput. Sci."},{"key":"ref_19","unstructured":"Fengjie, H. (2020). Research on LightGBM based network intrusion detection system, Xidian University."},{"key":"ref_20","first-page":"478","article-title":"Network based intrusion detection using the UNSW-NB15 dataset","volume":"8","author":"Meftah","year":"2019","journal-title":"Int. J. Comput. Digit. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-020-00379-6","article-title":"Performance analysis of intrusion detection systems using a feature selection method on the UNSW-NB15 dataset","volume":"7","author":"Kasongo","year":"2020","journal-title":"J. Big Data"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cao, B., Li, C., Song, Y., Qin, Y., and Chen, C. (2022). Network Intrusion Detection Model Based on CNN and GRU. Appl. Sci., 12.","DOI":"10.3390\/app12094184"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Alzaqebah, A., Aljarah, I., Al-Kadi, O., and Dama\u0161evi\u010dius, R. (2022). A Modified Grey Wolf Optimization Algorithm for an Intrusion Detection System. Mathematics, 10.","DOI":"10.3390\/math10060999"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1145\/502807.502809","article-title":"Searching in high-dimensional spaces: Index structures for improving the performance of multimedia databases","volume":"33","author":"Berchtold","year":"2001","journal-title":"ACM Comput. Surv. CSUR"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1080\/01431161.2020.1807650","article-title":"Information-theoretic feature selection with segmentation-based folded principal component analysis (PCA) for hyperspectral image classification","volume":"42","author":"Uddin","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e2","DOI":"10.23915\/distill.00002","article-title":"How to use t-SNE effectively","volume":"1","author":"Wattenberg","year":"2016","journal-title":"Distill"},{"key":"ref_27","unstructured":"Mengxiao, J. (2020). Research on network traffic anomaly detection algorithm based on SDN, Nanjing University of Posts and Telecommunications."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Alghushairy, O., Alsini, R., Soule, T., and Ma, X. (2021). A review of local outlier factor algorithms for outlier detection in big data streams. Big Data Cogn. Comput., 5.","DOI":"10.3390\/bdcc5010001"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"012038","DOI":"10.1088\/1742-6596\/1751\/1\/012038","article-title":"Implementation of K-means algorithm for clustering corn planting feasibility area in south lampung regency","volume":"1751","author":"Aldino","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ahmadov, E.Y. (2021). Comparative Aanalysis of K-Means, K-Means++ and Mini Batch K-Means Algorhitms in Phyton Environment. Probl. Inf. Technol., 119\u2013128.","DOI":"10.25045\/jpit.v12.i2.11"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"119708","DOI":"10.1016\/j.energy.2020.119708","article-title":"Well production forecasting based on ARIMA-LSTM model considering manual operations","volume":"220","author":"Fan","year":"2021","journal-title":"Energy"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/7\/238\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:47:23Z","timestamp":1760140043000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/7\/238"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,9]]},"references-count":31,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["a15070238"],"URL":"https:\/\/doi.org\/10.3390\/a15070238","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,9]]}}}