Supervised Deep Learning Models for Detecting GPS Spoofing Attacks on Unmanned Aerial Vehicles
Tala Talaei Khoei, Ghilas Aissou, Khair Al Shamaileh, V. Devabhaktuni, Naima Kaabouch
Abstract
Unmanned Aerial Networks (UAVs) are prone to several cyber-atttacks, including Global Positioining Spoofing attacks. For this purpose, numerous studies have been conducted to detect, classify, and mitigate these attacks, using Artificial Intelligence technqiues; howver, most of these studies provided techniques with low detection, high misdetection, and high bias rates. To fill this gap, in this paper, we propose three supervised deep learning techniques, namely Deep Neural Network, U Neural Network, and Long Short Term Memory. These models are evaluated in terms of Accuracy, Detection Rate, Misdetection Rate, False Alarm Rate, Training Time per Sample, Prediction Time, and Memory Size. The simulation results indicated that the U Neural Network outperforms other models with accuracy of 98.80%, a probability of detection of 98.85%, a misdetection of 1.15%, a false alarm of 1.8%, a training time per sample of 0.22 seconds, a prediction time of 0.2 seconds, and a memory size of 199.87 MiB. In addition, these results depicted that the Long Short Term Memory model provides the lowest performance among other models for detecting these attacks on UAVs.