Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Technologies

Lykou, Georgia; Moustakas, Dimitrios; Gritzalis, Dimitris

doi:10.3390/s20123537

Cited by 178 publications

(109 citation statements)

References 66 publications

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“…For instance, although wired sensors are efficient, they are expensive and difficult to manipulate in terms of installation, particularly for wide geographic distributions. On the contrary, wireless sensors may be easier to use but are subject to distortion due to environmental conditions and the presence of a higher level of electromagnetic disturbance, and are more vulnerable to cyber-attacks than wired sensors [53,54]. In addition, drone sensors, such as those found on Unmanned Aerial Vehicles (UAVs), are unstable.…”

Section: Technologiesof Detection Sensorsmentioning

confidence: 99%

Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

et al. 2021

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To ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future.

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Section: Technologiesof Detection Sensorsmentioning

confidence: 99%

Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

et al. 2021

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“…A series of accidents caused by "black flight" warn people about the urgency to counter UAV quickly and effectively. The stateof-the-art UAV detection methods include radar, acoustic, video and radio frequency (RF) detection [1][2][3][4][5][6][7][8], with Table 1 summarizing these trends. Regarding the radar-based techniques, UAVs are slow-moving and low-altitude-flying platforms that have a small radar cross-section, i.e., Low-Slow-Small (LSS) platforms, posing a challenge for radar detection.…”

Section: Introductionmentioning

confidence: 99%

Recognition of UAV Video Signal Using RF Fingerprints in the Presence of WiFi Interference

et al. 2021

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This paper investigates the problem of unmanned aerial vehicle (UAV) recognition in the presence of WiFi interference using passive radio frequency (RF) detection. The proposed method relies on machine learning based RF recognition and considers the scenario in the bandwidth of the video signal (VS) and WiFi are identical. Our machine learning strategy involves extracting 31 features from the WiFi signal and the UAV VS, which are then input to the classifier. Among the 31 features, 30 are statistical in the time and frequency domain, while the remaining one involves the effective subcarrier feature. We evaluate four different machine learning (ML) classifier variants and demonstrate through simulation and experiments that the proposed method can accurately recognize UAV VS in the presence of WiFi interference. We also improve the feature-vector compactness and reduce the 31-feature vector to a 6-feature vector composed of the most significant features and demonstrate that the recognition performance of random forest method (RandF) classifier is not compromised. The RandF obtains the best result, which has a recognition rate of 100% in the indoor experiment. While in the 2 km outdoor experiment, the recognition rate of the four ML classifiers is larger than 95.52%, which is better than other UAV detection methods such as radar, acoustic and video.INDEX TERMS UAV video signal, recognition, WiFi interference, machine learning.Ming Zuo Author received Bachelor's degree in Communication Engineering from Jiangxi Normal University, Nanchang, China, in 2014, and he Master's degree in Electronic and Communication Engineering from Nanchang University, Nanchang, China, in 2016. He is currently pursuing the PHD degree with the School of Electronic and Information Engineering, Beihang University. His research interests focus on radar signal processing, electronic countermeasure, passive positioning, anti-unmanned aerial vehicles.

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“…The detection, localization, tracking and classification of threats at short ranges have become the key requirements for surveillance systems intending to protect critical infrastructures, as well as private premises from intruders or hostile incursions, with an emphasis on unmanned aerial vehicles (UAV) [1][2][3][4]. To this purpose, different sensing technologies might be employed, including audio, video, infrared (IR), radio frequency (RF) and radar sensors, a combination of two or more technologies being a preferable solution for improving the system performance and increasing its reliability.…”

Section: Introductionmentioning

confidence: 99%

Fusing Measurements from Wi-Fi Emission-Based and Passive Radar Sensors for Short-Range Surveillance

et al. 2021

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In this work, we consider the joint use of different passive sensors for the localization and tracking of human targets and small drones at short ranges, based on the parasitic exploitation of Wi-Fi signals. Two different sensors are considered in this paper: (i) Passive Bistatic Radar (PBR) that exploits the Wi-Fi Access Point (AP) as an illuminator of opportunity to perform uncooperative target detection and localization and (ii) Passive Source Location (PSL) that uses radio frequency (RF) transmissions from the target to passively localize it, assuming that it is equipped with Wi-Fi devices. First, we show that these techniques have complementary characteristics with respect to the considered surveillance applications that typically include targets with highly variable motion parameters. Therefore, an appropriate sensor fusion strategy is proposed, based on a modified version of the Interacting Multiple Model (IMM) tracking algorithm, in order to benefit from the information diversity provided by the two sensors. The performance of the proposed strategy is evaluated against both simulated and experimental data and compared to the performance of the single sensors. The results confirm that the joint exploitation of the considered sensors based on the proposed strategy largely improves the positioning accuracy, target motion recognition capability and continuity in target tracking.

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Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Technologies

Cited by 178 publications

References 66 publications

Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

Recognition of UAV Video Signal Using RF Fingerprints in the Presence of WiFi Interference

Fusing Measurements from Wi-Fi Emission-Based and Passive Radar Sensors for Short-Range Surveillance

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