Intrusion Detection Method of Multi-channel Autoencoder Deep Learning

Abstract

Aiming at the shortcomings of the existing intrusion detection methods in detection accuracy and false alarm rate, an intrusion detection method of multi-channel autoencoder deep learning is proposed. The method is divided into two stages: unsupervised learning and supervised learning. Firstly, two independent autoencoders are trained by normal traffic and attack traffic respectively, and the two new feature vectors reconstructed and the original samples form a multi-channel eigenvector representation. Then, the 1-D convolutional neural network (CNN) is used to process the multi-channel eigenvector representation, and the possible dependence between channels is learned to better distinguish the difference between normal traffic and attack traffic. The proposed method combines unsupervised multi-channel feature learning and supervised cross-channel feature dependence learning to train a flexible and effective intrusion detection model, which greatly improves the accuracy of model detection. At the same time, in order to optimize the parameters of CNN and improve the identification effect of network on channel dependence, genetic algorithm is used to automatically find the optimal topology set of CNN model. The experimental results show that the proposed method achieves good results in multiple data sets and has better prediction accuracy than other intrusion detection algorithms.