{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T15:20:55Z","timestamp":1780413655100,"version":"3.54.1"},"reference-count":31,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,3,19]],"date-time":"2020-03-19T00:00:00Z","timestamp":1584576000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LY20F020030"],"award-info":[{"award-number":["LY20F020030"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51708487"],"award-info":[{"award-number":["51708487"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010248","name":"Zhejiang Province Public Welfare Technology Application Research Project","doi-asserted-by":"publisher","award":["LGF18F010008"],"award-info":[{"award-number":["LGF18F010008"]}],"id":[{"id":"10.13039\/501100010248","id-type":"DOI","asserted-by":"publisher"}]},{"name":"New Century 151 Talent Project of Zhejiang Province","award":["-"],"award-info":[{"award-number":["-"]}]},{"name":"Zhejiang Provincial Key Laboratory of Information Security Opening Fund","award":["-"],"award-info":[{"award-number":["-"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The Internet of Things (IoT) is widely applied in modern human life, e.g., smart home and intelligent transportation. However, it is vulnerable to malicious attacks, and the current existing security mechanisms cannot completely protect the IoT. As a security technology, intrusion detection can defend IoT devices from most malicious attacks. However, unfortunately the traditional intrusion detection models have defects in terms of time efficiency and detection efficiency. Therefore, in this paper, we propose an improved linear discriminant analysis (LDA)-based extreme learning machine (ELM) classification for the intrusion detection algorithm (ILECA). First, we improve the linear discriminant analysis (LDA) and then use it to reduce the feature dimensions. Moreover, we use a single hidden layer neural network extreme learning machine (ELM) algorithm to classify the dimensionality-reduced data. Considering the high requirement of IoT devices for detection efficiency, our scheme not only ensures the accuracy of intrusion detection, but also improves the execution efficiency, which can quickly identify the intrusion. Finally, we conduct experiments on the NSL-KDD dataset. The evaluation results show that the proposed ILECA has good generalization and real-time characteristics, and the detection accuracy is up to 92.35%, which is better than other typical algorithms.<\/jats:p>","DOI":"10.3390\/s20061706","type":"journal-article","created":{"date-parts":[[2020,3,19]],"date-time":"2020-03-19T03:54:14Z","timestamp":1584590054000},"page":"1706","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["An Improved LDA-Based ELM Classification for Intrusion Detection Algorithm in IoT Application"],"prefix":"10.3390","volume":"20","author":[{"given":"Dehua","family":"Zheng","sequence":"first","affiliation":[{"name":"Faculty of Mechanical Engineering &amp; Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9956-3732","authenticated-orcid":false,"given":"Zhen","family":"Hong","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China"},{"name":"School of Electrical &amp; Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Public Administration, Zhejiang University of Finance &amp; Economics, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang Provincial Testing Institute of Electronic Information Products, Hangzhou 310007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LSENS.2019.2918331","article-title":"Wireless communication modeling for the deployment of tiny IoT devices in rocky and mountainous environments","volume":"3","author":"Olasupo","year":"2019","journal-title":"IEEE Sens. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1109\/TCE.2019.2903351","article-title":"SensPnP: Seamless integration of heterogeneous sensors with IoT devices","volume":"65","author":"Roy","year":"2019","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/j.ins.2018.02.005","article-title":"Privacy-preserving smart IoT-based healthcare big data storage and self-adaptive access control system","volume":"479","author":"Yang","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_4","first-page":"8182","article-title":"IoT: Internet of threats? A survey of practical security vulnerabilities in real iot devices","volume":"6","author":"Meneghello","year":"2019","journal-title":"IEEE IoT J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1016\/j.future.2016.12.028","article-title":"A light weight authentication protocol for IoT-enabled devices in distributed cloud computing environment","volume":"78","author":"Ruhul","year":"2018","journal-title":"Future Gener. Comp. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1109\/TETC.2016.2633228","article-title":"A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks","volume":"7","author":"Pajouh","year":"2019","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.cose.2017.08.016","article-title":"A cybersecurity framework to identify malicious edge device in fog computing and cloud-of-things environments","volume":"74","author":"Amandeep","year":"2018","journal-title":"Comput. Secur."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"118556","DOI":"10.1109\/ACCESS.2019.2917135","article-title":"BRIoT: Behavior rule specification-based misbehavior detection for IoT-embedded cyber-physical systems","volume":"7","author":"Sharma","year":"2019","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1109\/TCYB.2018.2866527","article-title":"Immune-endocrine system inspired hierarchical coevolutionary multiobjective optimization algorithm for IoT service","volume":"50","author":"Yang","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"64411","DOI":"10.1109\/ACCESS.2019.2916886","article-title":"A multimodal malware detection technique for Android IoT devices using various features","volume":"7","author":"Kumar","year":"2019","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, K., Gao, M., Ouyang, Z., and Chen, S. (2015). LKM: A LDA-based k-means clustering algorithm for data analysis of intrusion detection in mobile sensor networks. Int. J. Distrib. Sens. Netw., 13.","DOI":"10.1155\/2015\/491910"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.knosys.2017.09.014","article-title":"An effective intrusion detection framework based on SVM with feature augmentation","volume":"136","author":"Wang","year":"2017","journal-title":"Knowl.-Based Syst."},{"key":"ref_13","first-page":"7702","article-title":"Privacy-preserving support vector machine training over blockchain-based encrypted IoT data in smart cities","volume":"6","author":"Shen","year":"2019","journal-title":"IEEE IoT J."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Subba, B., Biswas, S., and Karmakar, S. (2016, January 4\u20136). A neural network based system for intrusion detection and attack classification. Proceedings of the 2016 Twenty Second National Conference on Communication (NCC), Guwahati, India.","DOI":"10.1109\/NCC.2016.7561088"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Barreto, R., Lobo, J., and Menezes, P. (2019, January 12\u201314). Edge Computing: A neural network implementation on an IoT device. Proceedings of the 2019 5th Experiment International Conference, Funchal (Madeira Island), Funchal, Portugal.","DOI":"10.1109\/EXPAT.2019.8876463"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1109\/TCE.2019.2899067","article-title":"Multitask learning of deep neural network-based keyword spotting for IoT devices","volume":"65","author":"Leem","year":"2019","journal-title":"IEEE Trans. Consum. Electr."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.eswa.2017.07.005","article-title":"A feature reduced intrusion detection system using ANN classifier","volume":"88","author":"Manzoor","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.ins.2017.06.007","article-title":"A novel weighted support vector machines multiclass classifier based on differential evolution for intrusion detection systems","volume":"414","author":"Aburomman","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_19","unstructured":"Zhang, M., Guo, J., Xu, B., and Gong, J. (2015, January 15\u201317). Detecting network intrusion using probabilistic neural network. Proceedings of the 2015 11th International Conference on Natural Computation (ICNC), Zhangjiajie, China."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Brown, J., Anwar, M., and Dozier, G. (2016, January 1\u20134). An evolutionary general regression neural network classifier for intrusion detection. Proceedings of the 2016 25th International Conference on Computer Communication and Networks (ICCCN), Waikoloa, HI, USA.","DOI":"10.1109\/ICCCN.2016.7568493"},{"key":"ref_21","unstructured":"Cheng, C., Tay, W.P., and Huang, G.B. (2012, January 10\u201315). Extreme learning machines for intrusion detection. Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, QLD, Australia."},{"key":"ref_22","first-page":"489","article-title":"Extreme learning machine","volume":"70","author":"Huang","year":"2006","journal-title":"Theory Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3127","DOI":"10.1007\/s13369-016-2112-8","article-title":"Multiclass ELM based smart trustworthy IDS for MANETs","volume":"41","author":"Singh","year":"2016","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Singh, R., Kumar, H., and Singla, R.K. (2015, January 21\u201322). Performance analysis of an intrusion detection system using Panjab university intrusion dataSet. Proceedings of the 2015 2nd International Conference on Recent Advances in Engineering & Computational Sciences (RAECS), Chandigarh, India.","DOI":"10.1109\/RAECS.2015.7453280"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1080\/00401706.1973.10489026","article-title":"Generalized inverse of matrices and its applications","volume":"15","author":"Banerjee","year":"1971","journal-title":"Technometrics"},{"key":"ref_26","first-page":"221","article-title":"Matrices: Theory and applications","volume":"32","author":"Serre","year":"2002","journal-title":"Mathematics"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"12060","DOI":"10.1109\/ACCESS.2017.2787719","article-title":"An effective two-step intrusion detection approach based on binary classification and k-NN","volume":"6","author":"Li","year":"2018","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hou, H.R., Meng, Q.H., and Zhang, X.N. (2018, January 20\u201324). A voting-near-extreme-learning-machine classification algorithm. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8545217"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lin, J., Zhang, Q., Sheng, G., Yan, Y., and Jiang, X. (2018, January 20\u201322). Prediction system for dynamic transmission line load capacity based on PCA and online sequential extreme learning machine. Proceedings of the 2018 IEEE International Conference on Industrial Technology (ICIT), Lyon, France.","DOI":"10.1109\/ICIT.2018.8352440"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.eswa.2017.05.050","article-title":"Extreme learning machines for credit scoring: An empirical eEvaluation","volume":"86","author":"Lessmann","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hwang, C.L., and Yoon, K. (1981). Methods for multiple attribute decision making. Multiple Attribute Decision Making, Springer.","DOI":"10.1007\/978-3-642-48318-9"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/6\/1706\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:07:53Z","timestamp":1760173673000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/6\/1706"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,19]]},"references-count":31,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["s20061706"],"URL":"https:\/\/doi.org\/10.3390\/s20061706","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,19]]}}}