@inproceedings{20ba37636a59472ca12aac689d54f469,
title = "Towards an Interpretable AI Framework for Advanced Classification of Unmanned Aerial Vehicles (UAVs)",
abstract = "With UAVs on the rise, accurate detection and identification are crucial. Traditional unmanned aerial vehicle (UAV) identification systems involve opaque decision-making, restricting their usability. This research introduces an RF-based Deep Learning (DL) framework for drone recognition and identification. We use cutting-edge eXplainable Artificial Intelligence (XAI) tools, SHapley Additive Explanations (SHAP), and Local Interpretable Model-agnostic Explanations(LIME). Our deep learning model uses these methods for accurate, transparent, and interpretable airspace security. With 84.59% accuracy, our deep-learning algorithms detect drone signals from RF noise. Most crucially, SHAP and LIME improve UAV detection. Detailed explanations show the model's identification decision-making process. This transparency and interpretability set our system apart. The accurate, transparent, and user-trustworthy model improves airspace security.",
keywords = "Airspace Security, Deep Learning, Drone Detection, Explainable AI, LIME, RF Signals, SHAP",
author = "Ekramul Haque and Kamrul Hasan and Imtiaz Ahmed and Alam, {Md Sahabul} and Tariqul Islam",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 21st IEEE Consumer Communications and Networking Conference, CCNC 2024 ; Conference date: 06-01-2024 Through 09-01-2024",
year = "2024",
doi = "10.1109/CCNC51664.2024.10454862",
language = "English (US)",
series = "Proceedings - IEEE Consumer Communications and Networking Conference, CCNC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "644--645",
booktitle = "2024 IEEE 21st Consumer Communications and Networking Conference, CCNC 2024",
}