A integrated fuzzy-neural network intrusion detection model
Xiaoyao Xie · Journal of Shandong University · 2011
With increasing serious situation of network security and network defects in the agreement itself,it is incompetent to use the traditional way of a firewall.A fuzzy neural network model of integrated intrusion detection is proposed to improve the ability of intrusion prevention in a network.First,the data stream is obtained from the network,and the fuzzy approach is used to perform data pre-processing on characteristics of invasion.Then,the training and testing data is received by the integrated fuzzy neural network module from the data pre-processing module.Through repeated training and learning,the weights of nodes in the sub-trees converge to determine values.When training is completed,the model is used to detect the network data.The response module receives the results of the fuzzy neural network module and makes the appropriate response.In the experiment,the network intrusion detection datasets,a part of KDDCUP99,are used to evaluate the integrated fuzzy neural network,and are compared to a single neural network model.On the whole,the result shows that the fuzzy neural network ensemble method results are more stable.It slightly reduces the false alarm rate,false negative rate and false positive rate and significantly improves on accuracy and ability of datasets generalization.