Using hybrid algorithm to improve intrusion detection in multi layer feed forward neural networks

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.