Drone Authentication System Using Radio Frequency Fingerprinting

Document Type

Conference Proceeding

Source of Publication

Procedia Computer Science

Publication Date

1-1-2026

Abstract

The widespread integration of unmanned aerial vehicles (UAVs) across domains such as logistics, surveillance, and emergency response has introduced critical security challenges, particularly unauthorized access, identity spoofing, and drone cloning. Traditional software-based authentication methods, including GPS tracking and encryption, have proven inadequate against advanced cyber-physical threats. This paper proposes a secure and automated drone authentication framework based on Radio Frequency (RF) fingerprinting, leveraging intrinsic hardware-level signal imperfections to generate unique and unclonable drone identities. Using Random Forest classifiers, the system captures, preprocesses, and analyses RF features to distinguish between authorized and unauthorized UAVs. Validation with real-world RF datasets demonstrates high classification accuracy and resilience to spoofing attempts, supporting the framework's deployment in critical airspace security environments.

ISSN

1877-0509

Publisher

Elsevier BV

Volume

278

First Page

417

Last Page

424

Disciplines

Computer Sciences

Keywords

Airspace Security, Cybersecurity, Drone Authentication, Machine Learning, Radio Frequency (RF) Fingerprinting, Random Forest, Signal Processing, Spoofing Detection, Unmanned Aerial Vehicles (UAVs)

Scopus ID

105041024465

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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