Research on Network Intrusion Detection Technology Based on Data Mining Technology

Abstract

In this paper, the technology of network intrusion detection based on data mining technology is studied. As the conventional BP neural network be used to establish the network intrusion detection techniques has some problems, because the BP neural network is easy to fall into minimum value and the accuracy is low, the paper uses particle swarm algorithm to optimize the BP neural network model, and uses dynamic inertia weight coefficient to determine the parameters of BP neural network. By using dynamic inertia weight coefficient to determine the parameters of BP neural network, by combining the network intrusion traffic characteristics and BP neural network parameters to encode to a particle, we achieved the parameters of the network intrusion traffic characteristics and BP neural network synchronization selection. By using the KDD CUP99 database of intrusion traffic data to train and test the model we proposed and the conventional model separately, the results show that the algorithm we proposed has better detection efficiency and detection accuracy.