Conference
A Novel Hybrid Framework for Realistic UAV Detection using a Mixed RF Signal Database
| العنوان: | A Novel Hybrid Framework for Realistic UAV Detection using a Mixed RF Signal Database |
|---|---|
| المؤلفون: | Merabtine, Nassima, Loscri, Valeria, Djenouri, Djamel, Latif, Shahid |
| المساهمون: | Self-organizing Future Ubiquitous Network (FUN), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), ANR-21-ASIA-0002,DEPOSIA,Intelligence Artificielle pour la détection et géolocalisation d'une source électromagnétique illégitime(2021) |
| المصدر: | IEEE Future Networks - FNWF ; FNWF 2024 - IEEE Future Networks World Forum ; https://hal.science/hal-04702908 ; FNWF 2024 - IEEE Future Networks World Forum, Oct 2024, Dubai, United Arab Emirates |
| بيانات النشر: | HAL CCSD |
| سنة النشر: | 2024 |
| مصطلحات موضوعية: | Anomaly Detection, drone detection, UAVs, Machine Learning, Cyber Critical Infrastructures, [INFO]Computer Science [cs] |
| جغرافية الموضوع: | Dubai, United Arab Emirates |
| الوصف: | International audience ; Advances in Unmanned Aerial Vehicles (UAVs) empower a plethora of applications but also raise significant security and privacy challenges. Effective UAVs detection systems are crucial for mitigating these risks. This paper deals with this problem and tackles the challenges associated with real-world testing and the limitations of existing simulation methodologies for validating and evaluating UAVs detection protocols. A novel, realistic, and extensible framework is introduced, which includes a MATLAB-based surveillance system, a Python-based detection module utilizing Stacked Denoising Autoencoder (SDAE) and Local Outlier Factor (LOF) algorithms, and a hybrid database of both real and synthetic wireless RF signals. The synthetic wireless dataset is generated by the proposed surveillance system module. The alignment between the synthetic and real data is validated with an average Mean Squared Error (MSE) of less than 0.25. The detection module proves highly effective, achieving 96% accuracy in correctly classifying Wi-Fi signals and 88% accuracy in identifying UAV signals as anomalies (outliers). This innovative approach facilitates ongoing research and development in UAV detection, with the extensibility to incorporate new RF signal types and UAV models. |
| نوع الوثيقة: | conference object |
| اللغة: | English |
| الاتاحة: | https://hal.science/hal-04702908 https://hal.science/hal-04702908v1/document https://hal.science/hal-04702908v1/file/An_Anomaly_Detection_Framework.pdf |
| Rights: | http://creativecommons.org/licenses/by/ ; info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.2038BB35 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |