Wireless Network Abnormal Traffic Detection Method Based on Deep Transfer Reinforcement Learning

With the continuous development of information technology, the network as the infrastructure of the information age has become an indispensable and vital aspect of our daily lives. With the popularization of 5G technology, the number of handheld devices has increased significantly. Although it has brought great convenience to our production and life, it has also introduced new security risks, making the network more likely to be infiltrated and attacked. Currently, abnormal network traffic detection technology has become a vital part of network security, effectively protecting the network and computer systems from intrusion and maintaining normal operation. In the network abnormal traffic detection experiment based on simulation, most researchers use public and well-known datasets, and different datasets contain different attack samples. When testing on different datasets, the model needs to be retrained, significantly increasing the consumption of computer resources. The paper proposes a wireless network abnormal traffic detection method based on the deep transfer adversarial environment dueling double deep Q-Network (DTAE-Dueling DDQN). First, use the old NSL-KDD dataset to train AE-Dueling DDQN and save the training model weights. Then, use the idea of fine-tuning, transfer the weight of the AE-Dueling DDQN training is completed to the target model, and fine-tune the target model using the newer AWID dataset in the WiFi environment. The experiment compares the current representative deep learning (DL) and deep reinforcement learning (DRL) methods. Experimental results show that our proposed method saves computer resources significantly and achieves good results in all evaluation indicators.

I. Introduction

The advent of the 5G era [1] has laid the foundation for expanding consumer-oriented applications to industry-oriented applications and brought great convenience to industry and commerce. However, the introduction of new technologies has brought unknown security risks to the network. Among them, the security challenge brought by the large connection feature in 5G is particularly prominent [2] –[4]. In addition, the number of Internet of things terminals has increased significantly, which is always online, making the data more accessible to be hijacked [5], stolen, or invaded by Trojans, making it a springboard for distributed denial of service attacks (DDoS) [6].