ML-based Secure Low-Power Communication in Adversarial Contexts

Guanqun Song,Ting Zhu

arxiv(2022)

引用 0|浏览1
暂无评分
摘要
As wireless network technology becomes more and more popular, mutual interference between various signals has become more and more severe and common. Therefore, there is often a situation in which the transmission of its own signal is interfered with by occupying the channel. Especially in a confrontational environment, Jamming has caused great harm to the security of information transmission. So I propose ML-based secure ultra-low power communication, which is an approach to use machine learning to predict future wireless traffic by capturing patterns of past wireless traffic to ensure ultra-low-power transmission of signals via backscatters. In order to be more suitable for the adversarial environment, we use backscatter to achieve ultra-low power signal transmission, and use frequency-hopping technology to achieve successful confrontation with Jamming information. In the end, we achieved a prediction success rate of 96.19%.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络