关键词: 网络安全, 入侵检测, 神经网络, 自组织映射, NSL-KDD数据集

Abstract:

To solve the problem of poor performance of network intrusion detection under unbalanced data, a new intrusion detection model based on cPCA and AMSOM is proposed. By setting a small number of classes as background data, cPCA can reduce the dimension and improve the classifier’s ability to recognize attacks on a small number of classes. AMSOM constructs a more flexible dynamic neuron network in the output layer and maintains the corresponding relationship between the two spaces, which solves the problem of misshapen in the training process of SOM and improves the recognition rate of the clustering results of output neurons. Using NSL-KDD dataset, the experimental results show that the proposed model has good performance against a few network attacks, with higher accuracy, recall rate and [F1] value.

Key words: network security, intrusion detection, neural network, self organizing map, NSL-KDD dataset