Using CNN-based Representation Learning Method for Malicious Traffic Identification
Yao Wang, Jing An, Wei Hua Huang · 2018
Malicious traffic identification plays an important role in network security. This paper first introduces the defects of several traffic identification methods. By several successful applications of the representation learning in the field of network security, a method based on representation learning is proposed to identify the raw traffic data. This method represents raw flow data as an image and uses a convolutional neural network for classification and identification, which does not require manual selection and extraction of features. The segmented raw flow can be directly used as input data for the flow identification system. Experimental results show that this method has high availability and high accuracy in malicious traffic identification.