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- W4313305567 abstract "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%." @default.
- W4313305567 created "2023-01-06" @default.
- W4313305567 creator A5027445238 @default.
- W4313305567 creator A5067866708 @default.
- W4313305567 date "2022-12-27" @default.
- W4313305567 modified "2023-09-23" @default.
- W4313305567 title "ML-based Secure Low-Power Communication in Adversarial Contexts" @default.
- W4313305567 doi "https://doi.org/10.48550/arxiv.2212.13689" @default.
- W4313305567 hasPublicationYear "2022" @default.
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