论文标题
基于ML的安全性低功率通信
ML-based Secure Low-Power Communication in Adversarial Contexts
论文作者
论文摘要
随着无线网络技术变得越来越流行，各种信号之间的相互干扰变得越来越严重和普遍。因此，通常情况下，通过占据通道来干扰其自身信号的传播。特别是在对抗环境中，干扰对信息传输的安全造成了巨大伤害。因此，我提出了基于ML的安全超低电源通信，这是一种使用机器学习来通过捕获过去无线流量的模式来预测未来无线流量的方法，以确保通过反向散射来确保信号的超低功率传输。为了更适合对抗环境，我们使用反向散射来实现超低功率信号传输，并使用频率跳跃技术来成功与堵塞信息进行对抗。最后，我们达到了96.19％的预测成功率。
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%.
