LAUSR

Network abnormal traffic detection can monitor the network environment in real time by extracting and analysing network traffic characteristics, and plays an important role in network security protection. In order to solve the problems that the existing detection methods cannot fully learn the spatio-temporal characteristics of data, the classification accuracy is not high, and the detection time and accuracy are susceptible to the influence of redundant data in the sample. Thus, this paper proposes a network abnormal detection method (PCSS) integrating principal component analysis (PCA) and single-stage headless face detector algorithms (SSH). PCSS applies the PCA algorithm to the data preprocessing to eliminate the interference of redundant data. At the same time, PCSS also combines feature fusion and SSH to enhance the feature extraction of unclear features data, and effectively improve the detection speed and accuracy. Simulation experiments based on IDS2017 and IDS2012 data sets are carried out in this paper. Experimental results show that PCSS is obviously superior to other detection models in detection speed and accuracy, which provides a new method for efficiently detecting traffic attacks.