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Performance Evaluation of Feed Forward Neural Network for Wired Equivalent Privacy/Wi-Fi Protected Access Protocols
Objective: Millions of people use wireless devices in their day to day diligences without knowing the security facets of Wireless Technology. The aim of our research is to enhance the execution of widely used wireless devices's protocols by examining their behavior with Feed Forward Neural Network. Fundamentally, Neural Network is a multilayer perceptron network. It processes the records one at a time and "learn" by comparing the obtained output with the actual output. Hidden layer neurons play a cardinal role in the performance of Back Propagation. The process of determining the number of hidden layer neurons is still obscure. The work is focused on performance evaluation of the hidden layer neurons for WEP (Wired Equivalent Privacy) and WPA (Wi-Fi Protected Access) protocols. Methods/Statistical Analysis: For this work, three network architectures have been picked out to perform the analysis. The research work is carried out by using Back Propagation Algorithm in Neural Network Toolbox on the data captured by using Wireshark tool. Findings: The behavior of various unlike hidden neurons is evaluated through simulation technique. Network performance is also diagnosed with the help of epochs and Mean Square Error (MSE). The performance of Neural Network is evaluated and outcomes indicate that hidden layer neurons affect the functioning of the network. Improvement: We would like to work with the parameter and learning of the Neural Network to achieve best results.
Back Propagation, Feed Forward Neural Network, Hidden Layer, Mean Square Error, Wi-Fi Protected Access, Wired Equivalent Privacy.
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