{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T15:32:46Z","timestamp":1761060766612,"version":"build-2065373602"},"reference-count":20,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,3,14]],"date-time":"2019-03-14T00:00:00Z","timestamp":1552521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>When identifying the key features of the network intrusion signal based on the GA-RBF algorithm (using the genetic algorithm to optimize the radial basis) to identify the key features of the network intrusion signal, the pre-processing process of the network intrusion signal data is neglected, resulting in an increase in network signal data noise, reducing the accuracy of key feature recognition. Therefore, a key feature recognition algorithm for network intrusion signals based on neural network and support vector machine is proposed. The principal component neural network (PCNN) is used to extract the characteristics of the network intrusion signal and the support vector machine multi-classifier is constructed. The feature extraction result is input into the support vector machine classifier. Combined with PCNN and SVM (Support Vector Machine) algorithms, the key features of network intrusion signals are identified. The experimental results show that the algorithm has the advantages of high precision, low false positive rate and the recognition time of key features of R2L (it is a common way of network intrusion attack) data set is only 3.18 ms.<\/jats:p>","DOI":"10.3390\/sym11030380","type":"journal-article","created":{"date-parts":[[2019,3,15]],"date-time":"2019-03-15T04:12:09Z","timestamp":1552623129000},"page":"380","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Key Feature Recognition Algorithm of Network Intrusion Signal Based on Neural Network and Support Vector Machine"],"prefix":"10.3390","volume":"11","author":[{"given":"Kai","family":"Ye","sequence":"first","affiliation":[{"name":"Department of Educational Affairs, Zhengzhou Institute of Technology, Zhengzhou 450044, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"592","DOI":"10.4218\/etrij.17.0116.0305","article-title":"Network intrusion detection based on directed acyclic graph and belief rule base","volume":"39","author":"Zhang","year":"2017","journal-title":"ETRI J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10489-014-0618-x","article-title":"An optimized classification algorithm by BP neural network based on PLS and HCA","volume":"43","author":"Jia","year":"2015","journal-title":"Appl. Intell."},{"key":"ref_3","first-page":"266","article-title":"A hybrid intrusion detection system based on multilayer artificial neural network and intelligent feature selection","volume":"44","author":"Mansouri","year":"2015","journal-title":"Arch. Med. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11276-015-1065-2","article-title":"Cross-layer based multiclass intrusion detection system for secure multicast communication of MANET in military networks","volume":"22","author":"Arthur","year":"2016","journal-title":"Wirel. Netw."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s10489-014-0599-9","article-title":"An immune optimization based real-valued negative selection algorithm","volume":"42","author":"Xiao","year":"2015","journal-title":"Appl. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2315","DOI":"10.1007\/s10489-017-1085-y","article-title":"A new evolutionary neural networks based on intrusion detection systems using multiverse optimization","volume":"48","author":"Benmessahel","year":"2015","journal-title":"Appl. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.sjbs.2016.09.001","article-title":"Margin based ontology sparse vector learning algorithm and applied in biology science","volume":"24","author":"Gao","year":"2017","journal-title":"Saudi J. Biol. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1016\/j.arabjc.2017.12.022","article-title":"Hemicellulose structural changes during steam pretreatment and biogradation of lentinus edodes","volume":"11","author":"Ge","year":"2017","journal-title":"Arab. J. Chem."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/MIM.2018.8278808","article-title":"Internet of things for smart ports: technologies and challenges","volume":"21","author":"Yang","year":"2018","journal-title":"IEEE Instrum. Meas. Mag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6703","DOI":"10.1109\/TVT.2015.2480244","article-title":"Host-based intrusion detection for vanets: a statistical approach to rogue node detection","volume":"65","author":"Zaidi","year":"2016","journal-title":"Trans. Veh. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s11276-014-0769-z","article-title":"A multi-hop heterogeneous cluster-based optimization algorithm for wireless sensor networks","volume":"21","author":"Hu","year":"2015","journal-title":"Wirel. Netw."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.knosys.2016.10.031","article-title":"An efficient instance selection algorithm to reconstruct training set for support vector machine","volume":"116","author":"Liu","year":"2017","journal-title":"Knowl.-Based Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4059","DOI":"10.1080\/01431161.2016.1207261","article-title":"Object-based urban structure type pattern recognition from Landsat TM with a Support Vector Machine","volume":"37","author":"Wieland","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","first-page":"69","article-title":"Network intrusion detection with Bat algorithm for synchronization of feature selection and support vector machines","volume":"46","author":"Cheng","year":"2016","journal-title":"Comput. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s41403-017-0025-9","article-title":"Enhanced Monthly Precipitation Forecasting Using Artificial Neural Network and Singular Spectrum Analysis Conjunction Models","volume":"2","author":"Kalteh","year":"2017","journal-title":"INAE Lett."},{"key":"ref_16","first-page":"79","article-title":"Optimized neural network for spectrum prediction using genetic algorithm in cognitive radio networks","volume":"65","author":"Supraja","year":"2018","journal-title":"Cluster Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.patcog.2016.01.012","article-title":"Human action recognition using genetic algorithms and convolutional neural networks","volume":"59","author":"Ijjina","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_18","first-page":"1","article-title":"Design of standard parts recycling system based on machine vision","volume":"76","author":"Zhang","year":"2017","journal-title":"Autom. Instrum."},{"key":"ref_19","first-page":"835","article-title":"Deep convolution neural network recognition algorithm based on improved fisher criterion","volume":"41","author":"Sun","year":"2015","journal-title":"J. Beijing Univ. Technol."},{"key":"ref_20","first-page":"6865","article-title":"Threat analysis of IoT networks Using artificial neural network intrusion detection system","volume":"42","author":"Hodo","year":"2017","journal-title":"Tetrahedron Lett."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/3\/380\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:38:55Z","timestamp":1760186335000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/3\/380"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,14]]},"references-count":20,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["sym11030380"],"URL":"https:\/\/doi.org\/10.3390\/sym11030380","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2019,3,14]]}}}