An Improved Bayesian Network Intrusion Detection Algorithm Based on Deep Learning

摘要

Based on the preprocessing of training data sets, this paper adopts the convolution neural network technology to realize the reduction target of the redundant attribute of the data set. An improved Bayesian network intrusion detection algorithm based on deep learning is proposed. In this algorithm, sliding window technique and relative Euclidean distance are defined, and the structure updating and parameter learning of Bayesian networks are adaptively carried out on the basis of computing mutual information between attributes. Experimental results show that the new algorithm can effectively improve the computational efficiency and the accuracy of intrusion detection.