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

Loye Lynn Ray and Henry Felch
Additional contact information
Loye Lynn Ray: University of Maryland, College Park, MD, USA
Henry Felch: University of Maine, Orono, ME, USA

International Journal of Strategic Information Technology and Applications (IJSITA), 2014, vol. 5, issue 3, 24-36

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.

Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 18/ijsita.2014070102 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jsita0:v:5:y:2014:i:3:p:24-36

Access Statistics for this article

International Journal of Strategic Information Technology and Applications (IJSITA) is currently edited by Mehdi Khosrow-Pour

More articles in International Journal of Strategic Information Technology and Applications (IJSITA) from IGI Global
Bibliographic data for series maintained by Journal Editor ().