基于输入通道拆分的无线通信网络对抗攻击多任务防御OA北大核心CSTPCD

In wireless communication networks,attacks come from various sources and exhibit a variety of characteristics because of the openness and sharing of the network.It is difficult for the traditional defense methods to deal with multiple attack modes simultaneously.In addition,the defense efficiency is low and the accuracy is unsatisfied when processing multimodal data.Therefore,a wireless communication network counter attack multi-task defense based on input channel splitting is proposed.The Morlet wavelet transform is used to convert wireless communication network signals into time-frequency images,the time-frequency images are split into three RGB channels by input channel splitting.By the improved self-attention generative adversarial network(SAGAN),a multi-defense model is established in combination with multi-task learning.The generator within this model extracts the spatiotemporal features of RGB channel time-frequency images by the spatial attention module and the temporal attention long short-term memory(LSTM)network module,and generates adversarial samples.The image types are identified by discriminators.When an attack is detected,the attack data is replaced with adversarial samples to achieve multi-task adversarial defense in wireless communication networks.Experimental results have shown that the proposed method can effectively convert wireless communication network signals into time-frequency images and generate adversarial samples effectively,so as to complete wireless communication network counter attack multi-task defense.