Cao Xiaopeng and Qu Hongyan, Xi’an University of Posts and Telecommunications, China

The massive network traffic and high-dimensional features affect detection performance. In order to improve the efficiency and performance of detection, whale optimization sparse autoencoder model (WO-SAE) is proposed. Firstly, sparse autoencoder performs unsupervised training on high-dimensional raw data and extracts low-dimensional features of network traffic. Secondly, the key parameters of sparse autoencoder are optimized automatically by whale optimization algorithm to achieve better feature extraction ability. Finally, gated recurrent unit is used to classify the time series data. The experimental results show that the proposed model is superior to existing detection algorithms in accuracy, precision, and recall. And the accuracy presents 98.69%. WO-SAE model is a novel approach that reduces the user’s reliance on deep learning expertise

Traffic anomaly detection, Feature extraction, Sparse autoencoder, Whale optimization algorithm