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aboyhw: 金币+5, ★★★★★最佳答案, 谢谢版主 2015-03-07 23:17:46
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aboyhw: 金币+5, ★★★★★最佳答案, 谢谢版主 2015-03-07 23:17:46
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Accession number: 20150500477813 Title: Network intrusion clustering based on Fuzzy C-Means and modified Kohonen neural network Authors: Ye, Hongwei1 Email author aboyhw@163.com; Zhang, Lianjiao1; Liu, Xiaozhang2 Author affiliation: 1 School of Electron and Information Engineering, Heyuan Polytechnic College, Dong Huan Road, Heyuan, China 2 School of Computer Science, Dongguan University of Technology, No.1, Daxue Rd Songshan Lake, Dongguan, China Corresponding author: Ye, Hongwei Source title: Computer Modelling and New Technologies Abbreviated source title: Comput. Model. New Technol. Volume: 18 Issue: 11 Issue date: 2014 Publication year: 2014 Pages: 154-158 Language: English ISSN: 14075806 E-ISSN: 14075814 Document type: Journal article (JA) Publisher: Transport and Telecommunication Institute, Lomonosova street 1, Riga, LV-1019, Latvia Abstract: Kohonen neural network recognizes and clarifies substantive network data, but with a long running time and a slow convergence process. To solve this problem, a network intrusion clustering method is presented in this paper. Specifically, the training data is pretreated using Fuzzy C-Means (FCM). Then some selected data will be trained with using Kohonen neural network. Meanwhile, to speed up the convergence process of Kohonen neural network and to form a better optimized network topology, a neighbourhood function is established for the competing neuron. Each neuron has neighbourhood topology collections. The data simulation results demonstrate the efficiency and effectiveness of the proposed algorithm. Number of references: 12 Main heading: Neurons Controlled terms: Electric network topology - Fuzzy systems - Topology Uncontrolled terms: Clustering methods - Convergence process - FCM - Kohonen neural networks - Neighbourhood topology - Network intrusions - Network topology - Slow convergences Classification code: 461.9 Biology - 703.1 Electric Networks - 961 Systems Science Database: Compendex Compilation and indexing terms, © 2015 Elsevier Inc. |
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