Intrusion Detection Using Deep Belief Network and Probabilistic Neural Network

This paper focuses on the problems existing in intrusion detection using neural network, including redundant information, large amount of data, long-time training, easy to fall into the local optimal. An intrusion detection method using deep belief network (DBN) and probabilistic neural network (PNN) is proposed. First, the raw data are converted to low-dimensional data while retaining the essential attributes of the raw data by using the nonlinear learning ability of DBN. Second, to obtain the best learning performance, particle swarm optimization algorithm is used to optimize the number of hidden-layer nodes per layer. Next, PNN is used to classify the low-dimensional data. Finally, the KDD CUP 1999 dataset is employed to test the performance of the method mentioned above. The experiment result shows that the method performs better than the traditional PNN, PCA-PNN and unoptimized DBN-PNN.