Critical national infrastructure sites, such as airports and nuclear facilities, have been subject to a number of airspace breaches from malicious unmanned aerial systems (UASs) in recent years. Not only can these incidents cause major disruptions, but they can have significant financial consequences. This article conducts a short review of security incidents caused by the malicious use of small UASs at airports and nuclear facilities. We conclude that the number of incidents of concern remain high and suggest the increased use of UAS with longer flight times, indicating a move away from commercially available platforms, which have limited flight times. The detection and classification methods presented in this review each have strong benefits but this is not withstanding their own challenges. Hence, this review recommends that more than one sensor type are considered in parallel for live systems. A comprehensive detection and classification system must extend further than typical commercially available platforms if we are to fully realize the threat. We also conclude that while successful countermeasure methods do exist there still remains many policy and legal challenges ahead for their implementation.

Unmanned Aerial Vehicles (UAVs) are changing the way major industries conduct business in a globally friendly and efficient manner. Along with these economic benefits come major security challenges. Gatwick Airport is one example that cost £50 million. 1000 flights were cancelled over a 36hr period in 2018. Radio Frequency (RF) fingerprinting is an approach for UAV detection and classification associated with longer detection distances. Classification of a UAV flight mode would provide police commanders with information to assist risk assessment. For example, intelligence operations could be associated with a UAV flying while transmitting video. In this paper we introduce RF fingerprinting using Power Spectral Density (PSD) and spectrograms. This work utilises the open DroneRF dataset, made up of signals from the Parrot Bebop, Parrot AR and DJI Phantom 3 and further broken down into flight modes including switched on; hovering; flying with and without video transmission. Signal representations for each class are treated as images and transfer learning is implemented through the extraction of features using a VGG16 Convolutional Neural Network (CNN) pre-trained on the ImageNet database. Machine learning classifiers Logistic Regression (LR), Support Vector Machine and Random Forest are evaluated in these experiments for 2 classes (UAV present or not), 4 classes (UAV type) and 10 classifications (UAV flight modes). We show that PSD representation has higher accuracy than the use of time domain spectrograms, producing 100% accuracy for UAV detection and 88.6% accuracy for UAV type classification with LR. For 10 classification including flight modes, LR with PSD produced 87.3% accuracy which is a 40% increase on prior work in the field by A1-Sa'd et al.. Overall an approach has been introduced using transfer learning through CNN feature extraction and machine learning classification which performs with high accuracy compared with previous work on the same DroneRF dataset.

Unmanned Aerial Vehicles (UAVs) undoubtedly pose many security challenges. We need only look to the December 2018 Gatwick Airport incident for an example of the disruption UAVs can cause. In total, 1000 flights were grounded for 36 h over the Christmas period which was estimated to cost over 50 million pounds. In this paper, we introduce a novel approach which considers UAV detection as an imagery classification problem. We consider signal representations Power Spectral Density (PSD); Spectrogram, Histogram and raw IQ constellation as graphical images presented to a deep Convolution Neural Network (CNN) ResNet50 for feature extraction. Pre-trained on ImageNet, transfer learning is utilised to mitigate the requirement for a large signal dataset. We evaluate performance through machine learning classifier Logistic Regression. Three popular UAVs are classified in different modes; switched on; hovering; flying; flying with video; and no UAV present, creating a total of 10 classes. Our results, validated with 5-fold cross validation and an independent dataset, show PSD representation to produce over 91% accuracy for 10 classifications. Our paper treats UAV detection as an imagery classification problem by presenting signal representations as images to a ResNet50, utilising the benefits of transfer learning and outperforming previous work in the field.

Small unmanned aerial systems (UASs) present many potential solutions and enhancements to industry today but equally pose a significant security challenge. We only need to look at the levels of disruption caused by UASs at airports in recent years. The accuracy of UAS detection and classification systems based on radio frequency (RF) signals can be hindered by other interfering signals present in the same frequency band, such as Bluetooth and Wi-Fi devices. In this paper, we evaluate the effect of real-world interference from Bluetooth and Wi-Fi signals concurrently on convolutional neural network (CNN) feature extraction and machine learning classification of UASs. We assess multiple UASs that operate using different transmission systems: Wi-Fi, Lightbridge 2.0, OcuSync 1.0, OcuSync 2.0 and the recently released OcuSync 3.0. We consider 7 popular UASs, evaluating 2 class UAS detection, 8 class UAS type classification and 21 class UAS flight mode classification. Our results show that the process of CNN feature extraction using transfer learning and machine learning classification is fairly robust in the presence of real-world interference. We also show that UASs that are operating using the same transmission system can be distinguished. In the presence of interference from both Bluetooth and Wi-Fi signals, our results show 100% accuracy for UAV detection (2 classes), 98.1% (+/−0.4%) for UAV type classification (8 classes) and 95.4% (+/−0.3%) for UAV flight mode classification (21 classes).

Small Unmanned Aerial Systems (UAS) usage is undoubtedly increasing at a significant rate. However, alongside this expansion is a growing concern that dependable low-cost counter measures do not exist. To mitigate a threat in a restricted airspace, it must first be known that a threat is present. With airport disruption from malicious UASs occurring regularly, low-cost methods for early warning are essential. This paper considers a low-cost early warning system for UAS detection and classification consisting of a BladeRF software-defined radio (SDR), wideband antenna and a Raspberry Pi 4 producing an edge node with a cost of under USD 540. The experiments showed that the Raspberry Pi using TensorFlow is capable of running a CNN feature extractor and machine learning classifier as part of an early warning system for UASs. Inference times ranged from 15 to 28 s for two-class UAS detection and 18 to 28 s for UAS type classification, suggesting that for systems that require timely results the Raspberry Pi would be better suited to act as a repeater of the raw SDR data, enabling the processing to be carried out on a higher powered central control unit. However, an early warning system would likely fuse multiple sensors. These experiments showed the RF machine learning classifier capable of running on a low-cost Raspberry Pi 4, which produced overall accuracy for a two-class detection system at 100% and 90.9% for UAS type classification on the UASs tested. The contribution of this research is a starting point for the consideration of low-cost early warning systems for UAS classification using machine learning, an SDR and Raspberry Pi.

Small Unmanned Aerial Systems (UASs) provide significant benefits to economies across the world on a daily basis but increased usage brings a number of security challenges. For example, identifying small UASs operating with malicious intent in a restricted airspace. Supervised learning techniques applied to radio frequency (RF) signals have been considered for the classification of UAS type with high accuracy but due to labelled data assume the UAS signal is already known. Unsupervised learning algorithms such as K-means clustering provide a potential for identifying small UAS signals which have not been seen before. The use of transfer learning and CNN feature extraction (FE) with spectrogram graphical signal representations have been successfully used in a supervised manner for medical diagnosis and audio classification. This research is the first application of transfer learning and CNN FE as a pre-cursor to an unsupervised learning algorithm. This paper shows that clustering graphical representations of the signal and utilising CNN FE with transfer learning produces the highest v-measure score 0.814 but at a cost of 6s in time. Small UASs can travel at speeds of 45 mph so timely detection is essential in many use cases. A decrease in 0.2 v-measure score using PSD graphical image representations of the RF signal and PCA initialisation allows the clustering time to complete in under 0.3s even in environments with active interference in the same band. This timely result could provide effective early warning with the cueing of a secondary sensor or supervised algorithm with higher classification accuracy.