The UAVs threat to airport security: risk analysis and mitigation

Pyrgies, John

doi:10.3926/jairm.127

Cited by 28 publications

(12 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the specific case of airport drone intrusions, EASA guidelines [5][6][7] represent the main reference in which some suggestions are provided for risk assessment [7]. Concerning research works, reference [29] has studied drone incidents in the vicinity of worldwide airports to deliver a quantitative and qualitative analyses, proposing possible mitigation measures. Instead, reference [30] discusses a resilience action plan for airport stakeholders to defend an airport against airborne threats from misused drones.…”

Section: Security Incidentmentioning

confidence: 99%

A Methodological Framework for the Risk Assessment of Drone Intrusions in Airports

et al. 2022

View full text Add to dashboard Cite

Drone expansion needs to be considered as a menace in cases of negligent, illicit, or non-cooperative use. In the case of airports, a complete protection against drone intrusion should rely on an intrusion management system, aiming at avoiding the closure of the airport. This system requires the setting of proper risk assessment methodologies for airport operations, to explicitly consider the features of drone intrusion, possibly from a quantitative point of view. This work proposes a methodological framework for the risk assessment of drone intrusions in airports, tailored on drone-intrusion features, airport features, and current operations, and considering both safety-related and security-related causes. The framework is based on the combination of model-based and data-driven approaches in order to: (i) estimate an airport vulnerability index, to measure the susceptibility of the airport to drone intrusions, based on reference datasets; (ii) specify a set of event trees to evaluate the risks of the different threat scenarios related to drone intrusions. The proposed methodological framework is applied to a concrete case study, related to Milan Malpensa airport. The achieved results show the effectiveness of the approach and elicit further requirements for counter-drone systems in airports based on the assessed risks.

show abstract

Section: Security Incidentmentioning

confidence: 99%

A Methodological Framework for the Risk Assessment of Drone Intrusions in Airports

et al. 2022

View full text Add to dashboard Cite

show abstract

“…If the target is in the fixed position (x T1 , y T1 , z T1 ) = (x T2 , y T2 , z T2 ), then the two unknown angles θ 1 and θ 2 -i. q. the exact target coordinates-can be calculated using the system of six equations above (2). Disturbances which are present during flight might make these equations unsolvable.…”

Section: Proposed Solutionmentioning

confidence: 99%

“…Unfortunately, as with any other technology, the development of UAVs provides not only benefits but also threats to privacy and public safety. UAVs pose a massive security risk to airports and other critical infrastructure [2,3]. They can also be used to smuggle illicit goods, such as drugs [4], or even be utilized as weapons by terrorists.…”

Section: Introductionmentioning

confidence: 99%

Hostile UAV Detection and Neutralization Using a UAV System

Rudys

Laučys

Ragulis

et al. 2022

Drones

View full text Add to dashboard Cite

The technologies of Unmanned Aerial Vehicles (UAVs) have seen extremely rapid development in recent years. UAV technologies are being developed much faster than the means of their legislation. There have been many means of UAV detection and neutralization proposed in recent research; nonetheless, all of them have serious disadvantages. The essential problems in the detection of UAVs is the small size of UAVs, weak radio wave reflection, weak radio signal, and sound emitting. The main problem of conventional UAV countermeasures is the short detection and neutralization range. The authors propose the concept of the airborne counter-UAV platform (consisting of several vehicles) with radar. We use a low-cost marine radar with a high resolution 2 m wide antenna, embedded into the wing. Radar scanning is implemented by changing the heading of the aircraft. For the countermeasures, the authors suggest using a small rotorcraft UAV carried by a bigger fixed-wing one. A mathematical model that allows the calculation of the coordinates of the detected drone while scanning the environment in a moving UAV with radar was created. Furthermore, the results of integrated radar performance with a detected drone and the results of successful neutralization experiments of different UAVs were achieved.

show abstract

“…In recent years, the use of different types of drones has received much attention due to their efficiency in applications such as airport security, the protection of its facilities, and integration into security and surveillance systems [4][5][6]. On the other hand, drones can also be considered a serious threat in these security areas, and therefore, it is important to develop an efficient approach to detect types of drones in these applications [7][8][9]. Such technologies can be used in airport security and any military systems to prevent drone intrusion or to ensure their security [7,10,11].…”

Section: Introductionmentioning

confidence: 99%

Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery

et al. 2022

View full text Add to dashboard Cite

Drones are becoming increasingly popular not only for recreational purposes but also in a variety of applications in engineering, disaster management, logistics, securing airports, and others. In addition to their useful applications, an alarming concern regarding physical infrastructure security, safety, and surveillance at airports has arisen due to the potential of their use in malicious activities. In recent years, there have been many reports of the unauthorized use of various types of drones at airports and the disruption of airline operations. To address this problem, this study proposes a novel deep learning-based method for the efficient detection and recognition of two types of drones and birds. Evaluation of the proposed approach with the prepared image dataset demonstrates better efficiency compared to existing detection systems in the literature. Furthermore, drones are often confused with birds because of their physical and behavioral similarity. The proposed method is not only able to detect the presence or absence of drones in an area but also to recognize and distinguish between two types of drones, as well as distinguish them from birds. The dataset used in this work to train the network consists of 10,000 visible images containing two types of drones as multirotors, helicopters, and also birds. The proposed deep learning method can directly detect and recognize two types of drones and distinguish them from birds with an accuracy of 83%, mAP of 84%, and IoU of 81%. The values of average recall, average accuracy, and average F1-score were also reported as 84%, 83%, and 83%, respectively, in three classes.

show abstract

The UAVs threat to airport security: risk analysis and mitigation

Cited by 28 publications

References 4 publications

A Methodological Framework for the Risk Assessment of Drone Intrusions in Airports

A Methodological Framework for the Risk Assessment of Drone Intrusions in Airports

Hostile UAV Detection and Neutralization Using a UAV System

Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery

Contact Info

Product

Resources

About