Intrusion Detection Method Based on Sparse Autoencoder

Abstract

In view of the low detection rate and high false alarm rate in the current imbalance of classification in intrusion detection, an intrusion detection algorithm based on sparse autoencoder is proposed. This method uses sparse autoencoder technology to build a classified model to complete the detection of different types of data labels, and to improve the detection effect by adjusting the parameters. The experiment uses the UNSW-NB15 data set to test the algorithm. The experimental results show that the algorithm has a higher detection rate and a lower false positive rate.