Identification of Encrypted and Malicious Network Traffic Based on One-Dimensional Convolutional Neural Network

Abstract The rapid development of the internet has brought a significant increase in network traffic, but the efficiency of categorizing different types of network traffic has lagged behind, which has downgraded cyber security. How to identify different dimensions of network traffic data with more efficiency and accuracy remains a challenging issue. We design a convolutional neural network model HexCNN-1D that combines normalized processing and attention mechanisms. By adding the attention mechanism modules Global Attention Block (GAB) and Category Attention Block (CAB), different dimensions were introduced to classify and recognize network traffic. By extracting effective load information from hexadecimal network traffic, we designed to identify most of the network traffic, including encrypted and malicious traffic data. The experimental results show that the average accuracy is 98.8%. This method can greatly improve the recognition rate of different dimensions of network traffic data.