ML-based Secure Low-Power Communication in Adversarial Contexts

TL;DR

This paper introduces a machine learning approach for secure, ultra-low power wireless communication in adversarial environments, utilizing backscatter and frequency-hopping to counteract jamming and predict traffic patterns.

Contribution

It presents a novel ML-based method combining backscatter and frequency-hopping for secure low-power wireless communication under jamming threats.

Findings

Prediction success rate of 96.19%

Effective countermeasure against jamming

Enhanced security in wireless transmission

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%.