Self-Supervised Federated GNSS Spoofing Detection with Opportunistic Data

TL;DR

This paper introduces a self-supervised federated learning approach using LSTM networks for GNSS spoofing detection, effectively balancing detection accuracy with privacy preservation and reducing reliance on labeled datasets.

Contribution

It proposes a novel self-supervised federated learning framework that enables privacy-preserving GNSS spoofing detection without extensive labeled data.

Findings

Outperforms existing position-based methods in detection accuracy

Preserves user privacy by local data processing

Reduces need for labeled datasets through self-supervision

Abstract

Global navigation satellite systems (GNSS) are vulnerable to spoofing attacks, with adversarial signals manipulating the location or time information of receivers, potentially causing severe disruptions. The task of discerning the spoofing signals from benign ones is naturally relevant for machine learning, thus recent interest in applying it for detection. While deep learning-based methods are promising, they require extensive labeled datasets, consume significant computational resources, and raise privacy concerns due to the sensitive nature of position data. This is why this paper proposes a self-supervised federated learning framework for GNSS spoofing detection. It consists of a cloud server and local mobile platforms. Each mobile platform employs a self-supervised anomaly detector using long short-term memory (LSTM) networks. Labels for training are generated locally through a spoofing-deviation prediction algorithm, ensuring privacy. Local models are trained independently, and only their parameters are uploaded to the cloud server, which aggregates them into a global model using FedAvg. The updated global model is then distributed back to the mobile platforms and trained iteratively. The evaluation shows that our self-supervised federated learning framework outperforms position-based and deep learning-based methods in detecting spoofing attacks while preserving data privacy.