Abstract: The defense performance of intrusion detection system is often affected by class unbalance data. In order to automatically extract data features of scarce categories to improve the accuracy of intrusion detection systems in identifying unknown network attacks, a cost constraint algorithm is proposed. Firstly, a deep neural network based on stacked autoencoder is built up, and sparse constraints on the neurons are added in the hidden layer. Secondly, the cost objective function is optimized by generating a cost matrix, and costs are assigned to imbalanced data features. Finally, the back propagation is used to finely tune the parameters of the neural network model to obtain the optimal feature vector. The simulation results show that, compared with the FAE algorithm and the NDAE algorithm, the cost constraint algorithm improves the intrusion detection accuracy and convergence for multi-dimensional and class imbalanced data.

Key words: intrusion detection, feature extraction, autoencoder, cost matrix, deep learning ,