Fusing Constrained Autoencoder and Random Forest for Unmanned Aerial Vehicle Attacks Detection

Unmanned aerial vehicle (UAV) attacks detection constitutes a pivotal task in maintaining robust cybersecurity. However, existing approaches face difficulties in handling nonlinear and high-dimensional data, which poses a challenge to accurate attacks detection. Therefore, we propose the fusing constrained autoencoder (AE) and random forest (RF) method for UAV attacks detection. Firstly, we perform the feature compression using constrained AE, and we consider the utilization of the compressed feature to extract valuable information from data. Secondly, to enhance the distinguishability of benign and malicious samples, we learn a hyperplane for each category samples using boundary constraint loss. Thirdly, we learn the clustering center of each category samples using clustering loss to achieve inter-class discriminability and intra-class compactness, thereby enhancing model robustness. During testing, we substantiate the model feasibility and universality on UAV dataset, simulation dataset, and SensorNetGuard dataset. Especially, our method achieves 98.51% accuracy for detecting UAV sensor attacks, as for SensorNetGuard dataset, our method can realize 100% accuracy for detecting attacks.