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As more aircraft are using the Automatic Dependent Surveillance-Broadcast (ADS-B) devices for navigation and surveillance, the risks of injection attacks are highly increasing. The exchanged ADS-B messages are neither encrypted nor authenticated while containing valuable operational information, which imposes high risk on the safety of the airspace. For this reason, we propose in this paper an SVM-based ADS-B message injection attack detection technique for UAV onboard implementation. First, we simulated several message injection attacks on real raw ADS-B data. Then, three Support Vector Machine (SVM) models were examined in terms of two types of assessment criteria, detection efficiency and model performance. The results show that the C-SVM model is the best fit for our application, with an accuracy of 95.32%. 

In this article, real-time jamming detection against unmanned aerial vehicles (UAVs) is proposed via the integration of a software-defined radio (SDR) with an on-board Raspberry Pi processor. The SDR is utilized for capturing and forwarding the radio frequency signals to a receiver module hosted in the processor. This module extracts signal features characterized by orthogonal frequency division multiplexing (OFDM) parameters, energy parameters, and signal-to-noise ratio (SNR) parameters. Upon feature extraction, the aforementioned module exploits a machine learning (ML) classifier for detecting and classifying four jamming types; namely, barrage, single-tone, successive-pulse, and protocol-aware. The resulting configuration yielded in an overall detection rate (DR) of 93% and a false alarm rate (FAR) of 1.1%, which are in proximity to their counterparts obtained during the validation stage of the receiver module. 

With the increasing use of Unmanned Aerial Vehicles in military and civilian applications, the security of this technology has become one of the critical concerns. UAVs’ positioning and navigation activities are highly dependent on Global Positioning Systems as they provide accurate locations for these vehicles. However, due to the civilian GPS signals being open and unencrypted, malicious users can target them in multiple ways, including by launching Global Positioning System spoofing attacks. To address this security issue, numerous techniques have been proposed to detect and classify these attacks, including supervised machine learning techniques. However, no studies have focused on unsupervised models to detect these attacks. In this paper, we compare the performance of several supervised models with that of unsupervised models in terms of accuracy, probability of detection, probability of misdetection, probability of false alarm, processing time, training time, prediction time, and memory size. The supervised models are Gaussian Naïve Bayes, Classification and Regression Decision Tree, Logistic Regression, Random Forest, Linear-Support Vector Machine, and Artificial Neural Network. The unsupervised models are Principal Component Analysis, K-means clustering, and Autoencoder. The results show that the Classification and Regression Decision Tree model outperforms the other supervised and unsupervised models in detecting and classifying GPS spoofing attacks.