{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T17:26:52Z","timestamp":1783099612662,"version":"3.54.6"},"reference-count":33,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,22]],"date-time":"2021-12-22T00:00:00Z","timestamp":1640131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key R&amp;D Program of China","award":["2017YFB080301"],"award-info":[{"award-number":["2017YFB080301"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Preventing network intrusion is the essential requirement of network security. In recent years, people have conducted a lot of research on network intrusion detection systems. However, with the increasing number of advanced threat attacks, traditional intrusion detection mechanisms have defects and it is still indispensable to design a powerful intrusion detection system. This paper researches the NSL-KDD data set and analyzes the latest developments and existing problems in the field of intrusion detection technology. For unbalanced distribution and feature redundancy of the data set used for training, some training samples are under-sampling and feature selection processing. To improve the detection effect, a Deep Stacking Network model is proposed, which combines the classification results of multiple basic classifiers to improve the classification accuracy. In the experiment, we screened and compared the performance of various mainstream classifiers and found that the four models of the decision tree, k-nearest neighbors, deep neural network and random forests have outstanding detection performance and meet the needs of different classification effects. Among them, the classification accuracy of the decision tree reaches 86.1%. The classification effect of the Deeping Stacking Network, a fusion model composed of four classifiers, has been further improved and the accuracy reaches 86.8%. Compared with the intrusion detection system of other research papers, the proposed model effectively improves the detection performance and has made significant improvements in network intrusion detection.<\/jats:p>","DOI":"10.3390\/s22010025","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T02:02:57Z","timestamp":1640224977000},"page":"25","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Deep Stacking Network for Intrusion Detection"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9126-3054","authenticated-orcid":false,"given":"Yifan","family":"Tang","sequence":"first","affiliation":[{"name":"School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lize","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leiting","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"31711","DOI":"10.1109\/ACCESS.2019.2903723","article-title":"Intrusion detection for IoT based on improved genetic algorithm and deep belief network","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"20255","DOI":"10.1109\/ACCESS.2018.2820092","article-title":"A new intrusion detection system based on fast learning network and particle swarm optimization","volume":"6","author":"Ali","year":"2018","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1109\/TNSM.2009.090604","article-title":"Histogram-based traffic anomaly detection","volume":"6","author":"Kind","year":"2009","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Shalev-Shwartz, S., and Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press.","DOI":"10.1017\/CBO9781107298019"},{"key":"ref_5","first-page":"446","article-title":"A study on NSL-KDD dataset for intrusion detection system based on classification algorithms","volume":"4","author":"Dhanabal","year":"2015","journal-title":"Int. J. Adv. Res. Comput. Commun. Eng."},{"key":"ref_6","first-page":"148","article-title":"A subset feature elimination mechanism for intrusion detection system","volume":"7","author":"Nkiama","year":"2016","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_7","unstructured":"Hodo, E., Bellekens, X., Hamilton, A., Tachtatzis, C., and Atkinson, R. (2017). Shallow and deep networks intrusion detection system: A taxonomy and survey. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Janarthanan, T., and Zargari, S. (2017, January 19\u201321). Feature selection in UNSW-NB15 and KDDCUP\u201999 datasets. Proceedings of the 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), Edinburgh, UK.","DOI":"10.1109\/ISIE.2017.8001537"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.neucom.2016.03.031","article-title":"An effective intrusion detection framework based on MCLP\/SVM optimized by time-varying chaos particle swarm optimization","volume":"199","author":"Bamakan","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107042","DOI":"10.1016\/j.comnet.2019.107042","article-title":"Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic","volume":"168","author":"Elmasry","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_11","first-page":"462","article-title":"Intrusion detection model using fusion of chi-square feature selection and multi class SVM","volume":"29","author":"Thaseen","year":"2017","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"50850","DOI":"10.1109\/ACCESS.2018.2868993","article-title":"A novel intrusion detection model for a massive network using convolutional neural networks","volume":"6","author":"Wu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_13","unstructured":"Muhammad, G., Hossain, M.S., and Garg, S. (2020). Stacked autoencoder-based intrusion detection system to combat financial fraudulent. IEEE Internet Things J."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yang, Y., Zheng, K., Wu, C., and Yang, Y. (2019). Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network. Sensors, 19.","DOI":"10.3390\/s19112528"},{"key":"ref_15","first-page":"12","article-title":"Adaptive intrusion detection based on boosting and na\u00efve Bayesian classifier","volume":"24","author":"Rahman","year":"2011","journal-title":"Int. J. Comput. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Syarif, I., Zaluska, E., Prugel-Bennett, A., and Wills, G. (2012, January 13\u201320). Application of bagging, boosting and stacking to intrusion detection. Proceedings of the 8th International Conference on Machine Learning and Data Mining in Pattern Recognition, Berlin, Germany.","DOI":"10.1007\/978-3-642-31537-4_46"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"82512","DOI":"10.1109\/ACCESS.2019.2923640","article-title":"An adaptive ensemble machine learning model for intrusion detection","volume":"7","author":"Gao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_18","unstructured":"KDDCup (2019, January 19). KDD Cup Dataset. Available online: http:\/\/kdd.ics.uci.edu\/databases\/kddcup99\/kddcup99.html."},{"key":"ref_19","unstructured":"(2021, December 06). NSL-KDD Dataset. Available online: https:\/\/www.unb.ca\/cic\/datasets\/nsl.html."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., and Ghorbani, A.A. (2009, January 8\u201310). A detailed analysis of the KDD CUP 99 data set. Proceedings of the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, Ottawa, ON, Canada.","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"ref_21","first-page":"1848","article-title":"A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection","volume":"2","author":"Revathi","year":"2013","journal-title":"Int. J. Eng. Res. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","article-title":"Stacked generalization","volume":"5","author":"Wolpert","year":"1992","journal-title":"Neural Netw."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Deng, L., He, X., and Gao, J. (2013, January 26\u201331). Deep stacking networks for information retrieval. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638239"},{"key":"ref_24","first-page":"523","article-title":"One-hot encoding and convolutional neural network based anomaly detection","volume":"59","author":"Jie","year":"2019","journal-title":"J. Tsinghua Univ. (Sci. Technol.)"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1016\/j.ymssp.2006.05.004","article-title":"Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing","volume":"21","author":"Sugumaran","year":"2007","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yu, L., Pan, Y., and Wu, Y. (2009, January 11\u201313). Research on data normalization methods in multi-attribute evaluation. Proceedings of the International Conference on Computational Intelligence and Software Engineering, Wuhan, China.","DOI":"10.1109\/CISE.2009.5362721"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.neucom.2012.09.049","article-title":"Feature selection algorithm based on bare bones particle swarm optimization","volume":"148","author":"Zhang","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_28","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_29","unstructured":"Pham, N.T., Foo, E., Suriadi, S., Jeffrey, H., and Lahza, H.F.M. (February, January 29). Improving performance of intrusion detection system using ensemble methods and feature selection. Proceedings of the Australasian Computer Science Week Multiconference, Brisbane, Australia."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kanakarajan, N.K., and Muniasamy, K. (2015, January 16\u201318). Improving the accuracy of intrusion detection using gar-forest with feature selection. Proceedings of the 4th International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA) 2015, Durgapur, India.","DOI":"10.1007\/978-81-322-2695-6_45"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tang, C., Luktarhan, N., and Zhao, Y. (2020). SAAE-DNN: Deep Learning Method on Intrusion Detection. Symmetry, 12.","DOI":"10.3390\/sym12101695"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"21954","DOI":"10.1109\/ACCESS.2017.2762418","article-title":"A deep learning approach for intrusion detection using recurrent neural networks","volume":"5","author":"Yin","year":"2017","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yang, Y., Zheng, K., Wu, C., Niu, X., and Yang, Y. (2019). Building an effective intrusion detection system using the modified density peak clustering algorithm and deep belief networks. Appl. Sci., 9.","DOI":"10.3390\/app9020238"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/25\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:50:58Z","timestamp":1760169058000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,22]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22010025"],"URL":"https:\/\/doi.org\/10.3390\/s22010025","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,22]]}}}