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ERIC Number: ED556663
Record Type: Non-Journal
Publication Date: 2014
Pages: 109
Abstractor: As Provided
Reference Count: N/A
ISBN: 978-1-3037-2872-3
ISSN: N/A
Using Hybrid Algorithm to Improve Intrusion Detection in Multi Layer Feed Forward Neural Networks
Ray, Loye Lynn
ProQuest LLC, D.C.S. Dissertation, Colorado Technical University
The need for detecting malicious behavior on a computer networks continued to be important to maintaining a safe and secure environment. The purpose of this study was to determine the relationship of multilayer feed forward neural network architecture to the ability of detecting abnormal behavior in networks. This involved building, training, and testing a neural network intrusion detection system model incorporating a hybrid algorithm composed on genetic and back propagation algorithms. The varying of the number of neurons in the architecture and hybrid algorithm provided quantitative data for determining these effects. It showed that changes in the model architecture affected the models ability to detect malicious behavior with a low failure rate. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]
ProQuest LLC. 789 East Eisenhower Parkway, P.O. Box 1346, Ann Arbor, MI 48106. Tel: 800-521-0600; Web site: http://www.proquest.com/en-US/products/dissertations/individuals.shtml
Publication Type: Dissertations/Theses - Doctoral Dissertations
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A