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Data on Electrical Engineering Reported by Researchers at Fujian University of Technology (Intrusion Detection Based On Hybrid Classifiers for Smart Grid)

  • Sep 28, 2021 3:06 pm GMT
Electronics Daily

<p>2021 SEP 27 (NewsRx) -- By a <org>News Reporter-Staff News</org> Editor at <org>Electronics Daily</org> -- A new study on Engineering - Electrical Engineering is now available. According to news reporting originating from <location value="LU/cn..fuzhou" idsrc="">Fuzhou</location>, People’s <location value="LC/cn" idsrc="">Republic of China</location>, by NewsRx correspondents, research stated, “In this paper, a novel intrusion detection method combining a deep learning-based method and a feature-based method is proposed for smart grid. Specifically, long short-term memory and extreme gradient boosting are adopted for intrusion detection, and the results are fused based on the accuracies of these two models.”</p> <p>Financial supporters for this research include <org>National Key Research</org> and Development Program of <location value="LC/cn" idsrc="">China</location>, Project of <org value="ACORN:1598154812" idsrc="">Fujian University of Technology</org>, <location value="LC/cn" idsrc="">China</location>, FCT/MCTES through national funds, EU funds, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ), <org>Key Research</org> and Development Program of <location value="LU/cn..jangsu" idsrc="">Jiangsu Province</location>, <location value="LC/cn" idsrc="">China</location>, <org>Industrial Internet Innovation Development Project</org>, <location value="LC/cn" idsrc="">China</location> (Edge Computing Test Bed).</p> <p>Our news editors obtained a quote from the research from the <org value="ACORN:1598154812" idsrc="">Fujian University of Technology</org>, “As the XGBoost method is sensitive to its parameters and unsuitable selections greatly degrade its performance, in this paper, a Bayesian method is proposed to optimize these parameters. Moreover, a crossover scheme in a genetic algorithm is introduced to reduce the impact of falling into a local optimum of Bayesian optimization.”</p> <p>According to the news editors, the research concluded: “Extensive experimental results show the effectiveness of the proposed algorithm.”</p> <p>This research has been peer-reviewed.</p> <p>For more information on this research see: Intrusion Detection Based On Hybrid Classifiers for Smart Grid. <em>Computers &amp; Electrical Engineering</em>, 2021;93. <em>Computers &amp; Electrical Engineering</em> can be contacted at: Pergamon-elsevier <org value="ACORN:2131026191" idsrc="">Science Ltd</org>, The Boulevard, <location>Langford Lane</location>, Kidlington, Oxford OX5 1GB, <location value="LS/gb.eng" idsrc="">England</location>. (<org>Elsevier</org> - <a href=""></a>; Computers &amp; Electrical Engineering - <a href="" target="_blank"></a>)</p> <p>The news editors report that additional information may be obtained by contacting <person>Guangjie Han</person>, <org value="ACORN:1598154812" idsrc="">Fujian University of Technology</org>, Fujian Key Lab Automot Elect &amp; Elect Dr, <location value="LU/cn..fuzhou" idsrc="">Fuzhou</location> 350118, People’s <location value="LC/cn" idsrc="">Republic of China</location>. Additional authors for this research include <person>Chunhe Song</person>, <person>Yingying Sun</person> and <person>Joel J. P. C. Rodrigues</person>.</p> <p>The direct object identifier (DOI) for that additional information is: <a href="" target="_blank"></a>. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.</p> <p></p> <p class="shirttail">(Our reports deliver fact-based news of research and discoveries from around the world.)</p> <p></p>

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