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ML-based Secure Low-Power Communication in Adversarial Contexts

Guanqun SongTing Zhu
Dec 2022
摘要
As wireless network technology becomes more and more popular, mutualinterference between various signals has become more and more severe andcommon. Therefore, there is often a situation in which the transmission of itsown signal is interfered with by occupying the channel. Especially in aconfrontational environment, Jamming has caused great harm to the security ofinformation transmission. So I propose ML-based secure ultra-low powercommunication, which is an approach to use machine learning to predict futurewireless traffic by capturing patterns of past wireless traffic to ensureultra-low-power transmission of signals via backscatters. In order to be moresuitable for the adversarial environment, we use backscatter to achieveultra-low power signal transmission, and use frequency-hopping technology toachieve successful confrontation with Jamming information. In the end, weachieved a prediction success rate of 96.19%.
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