Multimodal deep neural network for UAV GPS jamming attack detection

Abstract

Despite the progress in Unmanned Aerial Vehicles, various issues remain related to their cybersecurity. One of these issues is GPS jamming attacks. GPS jamming attacks can cause UAVs to lose control and crash. These crashes may result in injuries or fatalities. In this paper, we propose a novel multimodal UAV GPS jamming attack detection framework capable of recognizing attacks from visual and tabular data using deep convolutional neural networks and a multi-layer perceptron, respectively. The proposed multimodal model is capable of not only detecting the presence of jamming attacks but also identifying five different types of such attacks. As a result of the experiments conducted, high results were obtained compared to the existing methods. Thus, MLP was able to detect GPS jamming attacks with 96.25 % accuracy, CNN with 94.66 % accuracy, and the proposed multimodal deep learning (MLP+CNN) with 99 % accuracy.