Machine Learning-Based Jamming Detection and Classification in Wireless Networks

Machine Learning-Based Jamming Detection and Classification in Wireless Networks

연구 분야: Infrastructure

학회: WiseML'23: Proceedings of the 2023 ACM Workshop on Wireless Security and Machine Learning

The development of novel tools to detect, classify and counteract the new generation of smart jammers in Internet of Things (IoT) is of paramount importance. Detection and classification have to be performed in a short time, with high reliability, and preserving the privacy of network users. In this work, we propose a novel machine learning (ML)-based jamming detection and classification algorithm which can be implemented in the network gateway (GW). The proposed method is based on energy detector (ED), the extraction of specific problem-tailored features, dimensionality reduction, and multi-class classification. Extensive numerical results have been carried out to evaluate the performance of detection and classification, varying the number of principal components selected through dimensionality reduction, the observation window length, the shadowing intensity, and the signal-to-jammer ratio (SJR). Our solution reaches remarkably high accuracy, i.e., up to 99%, outperforming a state-of-the-art solution. That is a very promising result considering that the approach does not need to inspect the decoded information, thus preserving the privacy of the network users.