{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:40:53Z","timestamp":1776444053757,"version":"3.51.2"},"reference-count":37,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,1,25]],"date-time":"2021-01-25T00:00:00Z","timestamp":1611532800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guizhou Province Project of Innovation Talents Teams of Electrostatic and Electromagnetic Protection","award":["[2017]5653"],"award-info":[{"award-number":["[2017]5653"]}]},{"name":"Guizhou Province Project of Innovation Talents Teams of Electrostatic and Electromagnetic Protection","award":["BOIMTLSHJD20161004"],"award-info":[{"award-number":["BOIMTLSHJD20161004"]}]},{"name":"Academician Liu Shanghe Fund of Electrostatic Protection Research","award":["[2017]5653"],"award-info":[{"award-number":["[2017]5653"]}]},{"name":"Academician Liu Shanghe Fund of Electrostatic Protection Research","award":["BOIMTLSHJD20161004"],"award-info":[{"award-number":["BOIMTLSHJD20161004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Security and Communication Networks"],"published-print":{"date-parts":[[2021,1,25]]},"abstract":"<jats:p>Traditional machine learning-based intrusion detection often only considers a single algorithm to identify intrusion data, lack of the flexibility method, low detection rate, no handing high-dimensional data, and cannot solve these problems well. In order to improve the performance of intrusion detection system, a novel general intrusion detection framework was proposed in this paper, which consists of five parts: preprocessing module, autoencoder module, database module, classification module, and feedback module. The data processed by the preprocessing module are compressed by the autoencoder module to obtain a lower-dimensional reconstruction feature, and the classification result is obtained through the classification module. Compressed features of each traffic are stored in the database module which can both provide retraining and testing for the classification module and restore these features to the original traffic for postevent analysis and forensics. For evaluation of the framework performance proposed, simulation was conducted with the CICIDS2017 dataset to the real traffic of the network. As the experimental results, the accuracy of binary classification and multiclass classification is better than previous work, and high-level accuracy was reached for the restored traffic. At the last, the possibility was discussed on applying the proposed framework to edge\/fog networks.<\/jats:p>","DOI":"10.1155\/2021\/6610675","type":"journal-article","created":{"date-parts":[[2021,1,25]],"date-time":"2021-01-25T18:35:15Z","timestamp":1611599715000},"page":"1-15","source":"Crossref","is-referenced-by-count":34,"title":["A Novel Framework Design of Network Intrusion Detection Based on Machine Learning Techniques"],"prefix":"10.1155","volume":"2021","author":[{"given":"Chongzhen","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanli","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Meng","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2338-2358","authenticated-orcid":true,"given":"Fangming","family":"Ruan","sequence":"additional","affiliation":[{"name":"School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Runze","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yidan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaru","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"issue":"2","key":"1","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/COMST.2015.2494502","article-title":"A survey of data mining and machine learning methods for cyber security intrusion detection","volume":"18","author":"A. 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