# Multi-Layer AI Sensor System for Real-Time GPS Spoofing Detection and Encrypted UAS Control

## TL;DR

This paper presents an AI-based sensor system that detects GPS spoofing and secures control of drones in real time, improving safety and reliability.

## Contribution

A novel multi-layer AI sensor framework for real-time GPS spoofing detection and encrypted UAS control with low latency and energy cost.

## Key findings

- The system achieves 99.99% detection accuracy and a 0.999 F1-score for spoofing detection.
- It uses PRESENT-128 encryption and CMAC authentication with 1.79 ms latency and 0.51 mJ energy cost.
- The framework is validated for real-world deployment in resource-constrained UAS environments.

## Abstract

Unmanned Aerial Systems (UASs) are playing an increasingly critical role in both civilian and defense applications. However, their heavy reliance on unencrypted Global Navigation Satellite System (GNSS) signals, particularly GPS, makes them highly susceptible to signal spoofing attacks, posing severe operational and safety threats. This paper introduces a comprehensive, AI-driven multi-layer sensor framework that simultaneously enables real-time spoofing detection and secure command-and-control (C2) communication in lightweight UAS platforms. The proposed system enhances telemetry reliability through a refined preprocessing pipeline that includes a novel GPS Drift Index (GDI), robust statistical normalization, cluster-constrained oversampling, Kalman-based noise reduction, and quaternion filtering. These sensing layers improve anomaly separability under adversarial signal manipulation. On this enhanced feature space, a differentiable architecture search (DARTS) approach dynamically generates lightweight neural network architectures optimized for fast, onboard spoofing detection. For secure command and control, the framework integrates a low-latency cryptographic layer utilizing PRESENT-128 encryption and CMAC authentication, achieving confidentiality and integrity with only 1.79 ms latency and a 0.51 mJ energy cost. Extensive experimental evaluations demonstrate the framework’s outstanding detection accuracy (99.99%), near-perfect F1-score (0.999), and AUC (0.9999), validating its suitability for deployment in real-world, resource-constrained UAS environments. This research advances the field of AI-enabled sensor systems by offering a robust, scalable, and secure navigation framework for countering GPS spoofing in autonomous aerial vehicles.