关键词: 自编码器, 栈示编码器, 特征提取, 置信度神经网络, 置信度损失函数 

Abstract: Network spoofing attack detection is an indispensable link in maintaining the normal operation of campus monitoring network, but the detection process is easily disturbed by problems such as signal strength, monitoring configuration and router performance. Therefore, a spoofing attack detection method of campus monitoring network based on deep learning algorithm is proposed. The self encoder in the deep learning network is used to reduce the dimension of the campus monitoring network traffic data, and the stack encoder composed of the self encoder is used to extract the features of the reduced dimension traffic data, the extracted features into is input the confidence neural network, the type of network spoofing attack is judged according to the comparison between the output confidence value and the fixed threshold, and the detection of campus monitoring network spoofing attack is completed. The experimental results show that the proposed method has the advantages of short detection time, high detection rate and low false alarm rate. 

Key words: autoencoder, stack encoder, feature extraction, confidence neural network, confidence loss function