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Improving Performance and Convergence Rates in Multi-Layer Feed Forward Neural Network Intrusion Detection Systems: A Review of the Literature

Improving Performance and Convergence Rates in Multi-Layer Feed Forward Neural Network Intrusion Detection Systems: A Review of the Literature

Loye Lynn Ray, Henry Felch
Copyright: © 2014 |Volume: 5 |Issue: 3 |Pages: 13
ISSN: 1947-3095|EISSN: 1947-3109|EISBN13: 9781466656697|DOI: 10.4018/ijsita.2014070102
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MLA

Ray, Loye Lynn, and Henry Felch. "Improving Performance and Convergence Rates in Multi-Layer Feed Forward Neural Network Intrusion Detection Systems: A Review of the Literature." IJSITA vol.5, no.3 2014: pp.24-36. http://doi.org/10.4018/ijsita.2014070102

APA

Ray, L. L. & Felch, H. (2014). Improving Performance and Convergence Rates in Multi-Layer Feed Forward Neural Network Intrusion Detection Systems: A Review of the Literature. International Journal of Strategic Information Technology and Applications (IJSITA), 5(3), 24-36. http://doi.org/10.4018/ijsita.2014070102

Chicago

Ray, Loye Lynn, and Henry Felch. "Improving Performance and Convergence Rates in Multi-Layer Feed Forward Neural Network Intrusion Detection Systems: A Review of the Literature," International Journal of Strategic Information Technology and Applications (IJSITA) 5, no.3: 24-36. http://doi.org/10.4018/ijsita.2014070102

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Abstract

Today's anomaly-based network intrusion detection systems (IDSs) are plagued with detecting new and unknown attacks. The review of the literature builds ideas for researching the problem of detecting these attacks using multi-layered feed forward neural network (MLFFNN) IDSs. The scope of the paper focused on a review of the literature from primarily 2008 to the present found in peer-review and scholarly journals. A key word search was used to compare and contrast the literature to find strengths, weaknesses and gaps. The significance of the research found that further work is needed to improve the performance and convergence rates of MLFFNN IDSs. This literature review contributes to the area of intrusion detection by looking at the effects of architecture, algorithms, and input data on the performance and convergence rates of MLFFNN IDSs.

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