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| Design Network Intrusion Detection System using Hybrid Fuzzy-Neural Network
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Source |
International Journal of Computer Science and Security (IJCSS) |
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Table of Contents |
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Volume: 4 Issue: 3 |
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Pages: 265-372 |
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Publication
Date: July 2010 |
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ISSN
(Online): 1985-1553 |
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Pages |
285 - 294 |
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Author(s) |
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Published
Date |
10-08-2010 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
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KEYWORDS: intrusion detection, neural network, fuzzy claustering |
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| As networks grow both in importance and size, there is an increasing need for effective security monitors such as Network Intrusion Detection System to prevent such illicit accesses. Intrusion Detection Systems technology is an effective approach in dealing with the problems of network security. In this paper, we present an intrusion detection model based on hybrid fuzzy logic and neural network. The key idea is to take advantage of different classification abilities of fuzzy logic and neural network for intrusion detection system. The new model has ability to recognize an attack, to differentiate one attack from another i.e. classifying attack, and the most important, to detect new attacks with high detection rate and low false negative. Training and testing data were obtained from the Defense Advanced Research Projects Agency (DARPA) intrusion detection evaluation data set |
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TechRepublic
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weblog.chrisricard.net
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Jaringan Informasi
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| muna mhammad taher jawhar : Colleagues
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| Monica Mehrotra : Colleagues
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