Best Paper Award

Yang M., Oracevic A., Dilek S.

The 2025 Ieee Smart Applications, Communications And Networking Conference (Smartnets 2025), Temmuz 2025

Ödülün Kapsamı: Bilimsel/Mesleki Çalışmalardan Alınan Ödül
Ödül Türü: Kongre, Konferans, Festival veya Sempozyum Kurullarınca Verilen Ödül
Ödül Veren Ülke: Türkiye
Ödülü Veren Organizasyon: The 2025 Ieee Smart Applications, Communications And Networking Conference (Smartnets 2025)
Araştırma Alanları: Bilgisayar Bilimleri, Bilgi Güvenliği ve Güvenilirliği, Yapay Zeka, Bilgisayarda Öğrenme ve Örüntü Tanıma, Mühendislik ve Teknoloji
Ödülün Tarihi: Temmuz 2025
Açıklama: <span style="font-size: 12pt; font-family: Arial, sans-serif; background-color: transparent; font-weight: 700; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; font-variant-emoji: normal; vertical-align: baseline; white-space-collapse: preserve;">Title: Deep Learning-Based Defense Against GPS Spoofing Attacks in Unmanned Aerial Vehicles<span style="font-size: 12pt; font-family: Arial, sans-serif; background-color: transparent; font-weight: 700; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; font-variant-emoji: normal; vertical-align: baseline; white-space-collapse: preserve;"> <span style="font-size: 12pt; font-family: Arial, sans-serif; background-color: transparent; font-style: italic; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; font-variant-emoji: normal; vertical-align: baseline; white-space-collapse: preserve;">IEEE Smart Applications, Communications and Networking Conference (SmartNets 2025)<span style="font-size: 12pt; font-family: Arial, sans-serif; background-color: transparent; font-style: italic; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; font-variant-emoji: normal; vertical-align: baseline; white-space-collapse: preserve;"> <a href="https://ieeexplore.ieee.org/document/11106869/"><span style="font-size: 12pt; font-family: Arial, sans-serif; color: rgb(17, 85, 204); background-color: transparent; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; font-variant-emoji: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space-collapse: preserve;">https://ieeexplore.ieee.org/document/11106869/</a><span style="font-size: 12pt; font-family: Arial, sans-serif; background-color: transparent; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; font-variant-emoji: normal; vertical-align: baseline; white-space-collapse: preserve;"> <span style="font-size: 12pt; font-family: Arial, sans-serif; background-color: transparent; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; font-variant-emoji: normal; vertical-align: baseline; white-space-collapse: preserve;">Abstract: Drones rely heavily on GPS sensors for navigation, making them vulnerable to GPS spoofing attacks that can manipulate flight paths and compromise operations. To address this critical challenge, we propose a novel detection method leveraging deep learning to identify spoofed GPS signals. Our detection model, BiLSTM-Attention-CNN, integrates a bidirectional long short-term memory (BiLSTM) layer to capture global temporal dependencies, an attention mechanism to focus on key sequence features, and a 1D convolutional neural network (1D-CNN) layer to extract local patterns effectively. We detail the development of a simulation environment using a modified version of JMAVSim to emulate realistic GPS spoofing scenarios. Using this setup, combined with QGroundControl for flight experiments, we systematically analyze various GPS spoofing techniques and collect comprehensive datasets for training and evaluation. Comparative experiments demonstrate the superiority of the BiLSTM-Attention-CNN model over both traditional machine learning and other deep learning approaches, achieving state-of-the-art performance in detecting GPS spoofing attacks. These findings underscore the potential of deep learning-based frameworks to enhance UAV navigation security.