关键词: 入侵检测, 深度学习, 反向传播神经网络, 梯度提升树

Abstract: Aiming at the problem of poor performance of traditional intelligent intrusion detection methods in massive data environment, a multi-classifier intrusion detection method (DBN-OGB) based on one-versus-one gradient boosting decision tree (GBDT) under deep belief networks (DBN) is proposed. This method first uses the deep belief network to extract the low dimension and representative characteristic data from the high-dimensional and complex intrusion detection data. Then, one-versus-one method is used to construct a gradient tree classifier between two kinds of characteristic data. The classifiers are used to identify the unknown network attack, and the category with the most votes is the category of the attack. Finally, the NSL-KDD data set is used to carry out simulation experiments. The experimental results show that the average accuracy and detection rate of the DBN-OGB method are higher than 99%. Compared with the DBN-MSVM method, the accuracy and detection rate of the method are increased by 0.56% and 1.03% respectively, indicating that DBN-OGB is an effective and feasible intrusion detection method, and can improve the detection performance of massive intrusion data.

Key words: intrusion detection, deep learning, back propagation neural network, gradient boosting decision tree