Malicious Network Traffic Recognition Method Based on Deep Learning

With the Internet technology developing rapidly, network security has attracted more and more attention. Therefore, in order to protect private information against attack from malware, many people focus on the process of analyzing and recognizing raw traffic data to send an alarm to system in time and prevent damage. In this paper, we propose an improved method to recognize malicious network traffic data based on deep learning neural network, which applied convolutional neural network combined with Squeeze-and-Excitation Networks in order to learn spatial feature of network traffic data effectively and accurately.