Scenario Analysis of Threats Posed to Critical Infrastructures by Civilian Drones

Schneider, Moritz; Lichte, Daniel; Witte, Dustin; Gimbel, Stephan; Brucherseifer, Eva

doi:10.3850/978-981-18-2016-8_234-cd

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…We advocate a scenario-specific hybrid detection system that satisfies the minimum scenario requirements. Based on the list of detection factors in Table II and [142], one can choose a minimum set of basic requirements for a specific scenario. In more general cases, we propose a minimum set of three requirements as these are generally employed in almost any detection scenario.…”

Section: Conclusion and Future Insightsmentioning

confidence: 99%

On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review

et al. 2022

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The market size of civilian drones is tremendously increasing and is expected to reach 1.66 million by the end of 2023. The increase in number of civilian drones poses several privacy and security threats. To safeguard critical assets and infrastructure and to protect privacy of people from the illegitimate uses of commercial drones, a drone detection system is inevitable. In particular, there is a need for a drone detection system that is efficient, accurate, robust, costeffective and scalable. Recognizing the importance of the problem, several drone detection approaches have been proposed over time. However, none of these provides sufficient performance due to the inherited limitations of the underlying detection technology. More specifically, there are trade-offs among various performance metrics e.g., accuracy, detection range, and robustness against environmental conditions etc. This motivates an in-depth study and critical analysis of the existing approaches, highlighting their potential benefits and limitations. In this paper, we provide a rigorous overview of the existing drone detection techniques and a critical review of the state-of-the-art. Based on the review, we provide key insights on the future drone detection systems. We believe these insights will provide researchers and practicing engineers a holistic view to understand the broader context of the drone detection problem.

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Section: Conclusion and Future Insightsmentioning

confidence: 99%

On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review

et al. 2022

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“…All scenario-factors are listed in the top row and their states in the columns below the respective factor (Johansen, 2018). For more examples of how to create a morphological box for scenario analysis, see Witte et al (2020) or Schneider et al (2021).…”

Section: Morphological Analysismentioning

confidence: 99%

A Probabilistic Approach to Dynamic Risk Scenario Identification

Schneider¹,

Ramírez-Agudelo²,

Halekotte³

et al. 2022

Book of Extended Abstracts for the 32nd European Safety and Reliability Conference

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Situation awareness is crucial for decision makers during an emergency. An efficient knowledge management can enhance situation awareness by providing information about the most relevant factors of the situation. Scenario analysis, based on morphological analysis, represents a structured method that can support the identification of such factors. Various studies based on this method have already been presented in the literature, e.g. as a method to strategically enhance disaster preparedness. In this paper, we introduce an approach that allows us to analyze current information in order to dynamically identify an emerging risk scenario. First, morphological analysis is applied to construct a scenario space. Second, in order to quantify the relations between scenario-factors, a Bayesian network model is implemented. For identification of the scenario, current information about the scenario-factors are needed. Information can be gathered from different sources, e.g. sensors or observations by emergency personnel and processed in the Bayesian network model to calculate the posterior probabilities of the parameters in the model. We illustrate the approach for risk scenario identification by applying it to an example in the context of emergency management. To conclude, we discuss the benefits and limitations of this approach as a knowledge management tool for enhancing situation awareness.

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“…Protecting private or safety-critical infrastructures against attacks (e.g., espionage) by unmanned aerial vehicles (UAVs or drones) is an important security issue given the increasing prevalence of UAVs in commercial and private sectors (e.g., [1][2][3]). Therefore, there is also an increasing need for robust detection systems capable of reliably identifying and locating unauthorized UAVs.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Simulation–Reality Gap for Deep Learning-Based Drone Detection

Dieter

Weinmann²,

Jäger

et al. 2023

Electronics

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The detection of drones or unmanned aerial vehicles is a crucial component in protecting safety-critical infrastructures and maintaining privacy for individuals and organizations. The widespread use of optical sensors for perimeter surveillance has made optical sensors a popular choice for data collection in the context of drone detection. However, efficiently processing the obtained sensor data poses a significant challenge. Even though deep learning-based object detection models have shown promising results, their effectiveness depends on large amounts of annotated training data, which is time consuming and resource intensive to acquire. Therefore, this work investigates the applicability of synthetically generated data obtained through physically realistic simulations based on three-dimensional environments for deep learning-based drone detection. Specifically, we introduce a novel three-dimensional simulation approach built on Unreal Engine and Microsoft AirSim for generating synthetic drone data. Furthermore, we quantify the respective simulation–reality gap and evaluate established techniques for mitigating this gap by systematically exploring different compositions of real and synthetic data. Additionally, we analyze the adaptation of the simulation setup as part of a feedback loop-based training strategy and highlight the benefits of a simulation-based training setup for image-based drone detection, compared to a training strategy relying exclusively on real-world data.

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Scenario Analysis of Threats Posed to Critical Infrastructures by Civilian Drones

Cited by 5 publications

References 13 publications

On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review

On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review

A Probabilistic Approach to Dynamic Risk Scenario Identification

Quantifying the Simulation–Reality Gap for Deep Learning-Based Drone Detection

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