现代电子技术2024，Vol.47Issue(11)：13-17,5.DOI:10.16652/j.issn.1004-373x.2024.11.003
基于输入通道拆分的无线通信网络对抗攻击多任务防御
Wireless communication network counter attack multi-task defense based on input channel splitting
摘要
Abstract
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.关键词
输入通道拆分/无线通信网络/对抗攻击/多任务防御/小波变换/注意力机制/生成对抗网络/长短期记忆网络Key words
input channel splitting/wireless communication network/counter attack/multi-task defense/wavelet transform/attention mechanism/GAN/LSTM分类
电子信息工程引用本文复制引用
高程昕,温昕,曹锐..基于输入通道拆分的无线通信网络对抗攻击多任务防御[J].现代电子技术,2024,47(11):13-17,5.基金项目
国家自然科学基金委员会青年科学基金:基于脑老化的多中心功能磁共振分析方法研究(62206196) （62206196）
山西省青年科学基金项目:神经影像大数据功能指纹挖掘及模型可解释性研究(202103021223035) （202103021223035）
