# ML-based Secure Low-Power Communication in Adversarial Contexts

arXiv: https://arxiv.org/abs/2212.13689 · HTML: https://mlsanity.com/paper/2212.13689/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2022.

## Built on by (most cited first, 3 of 3)

- [On-device Large Multi-modal Agent for Human Activity Recognition](https://mlsanity.com/paper/2512.19742/) (2025)
- [Data Classification With Multiprocessing](https://mlsanity.com/paper/2312.15152/) (2023)
- [Beyond the Frame: Single and mutilple video summarization method with user-defined length](https://mlsanity.com/paper/2401.10254/) (2023)

## Abstract (arXiv)

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%.