JamShield: A Machine Learning Detection System for Over-the-Air Jamming Attacks

TL;DR

JamShield is a real-world, machine learning-based system that detects wireless jamming attacks more accurately and efficiently by using over-the-air data, hybrid feature selection, and adaptive auto-classification.

Contribution

It introduces a novel detection system trained on real over-the-air data with hybrid feature selection and dynamic auto-classification, improving detection performance in practical scenarios.

Findings

Significant improvement in detection rate, precision, and recall.

Reduction in false alarms and misdetections.

Robust performance in real-world wireless networks.

Abstract

Wireless networks are vulnerable to jamming attacks due to the shared communication medium, which can severely degrade performance and disrupt services. Despite extensive research, current jamming detection methods often rely on simulated data or proprietary over-the-air datasets with limited cross-layer features, failing to accurately represent the real state of a network and thus limiting their effectiveness in real-world scenarios. To address these challenges, we introduce JamShield, a dynamic jamming detection system trained on our own collected over-the-air and publicly available dataset. It utilizes hybrid feature selection to prioritize relevant features for accurate and efficient detection. Additionally, it includes an auto-classification module that dynamically adjusts the classification algorithm in real-time based on current network conditions. Our experimental results demonstrate significant improvements in detection rate, precision, and recall, along with reduced false alarms and misdetections compared to state-of-the-art detection algorithms, making JamShield a robust and reliable solution for detecting jamming attacks in real-world wireless networks.