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![Fig. 4: Man-In-The-Middle Attack On Drone [32].](profile/Vinay-Chamola/publication/353212475/figure/fig2/AS:1045064184758273@1626174126083/Man-In-The-Middle-Attack-On-Drone-32_Q320.jpg)
![Fig. 5: GPS spoofing attack on GPS Enabled Drone [36].](profile/Vinay-Chamola/publication/353212475/figure/fig3/AS:1045064184774656@1626174126142/GPS-spoofing-attack-on-GPS-Enabled-Drone-36_Q320.jpg)
![Fig. 6: Jamming Attacks On Drone [45].](profile/Vinay-Chamola/publication/353212475/figure/fig4/AS:1045064184774657@1626174126199/Jamming-Attacks-On-Drone-45_Q320.jpg)
Drone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we...
Contexts in source publication
Context 1
... in the jamming attack radio signals are used to attack the drones which mainly affect the physical layer. Signals of Wi-Fi and Bluetooth can be easily jammed, and that too using a low power jammer [42]. The ability of the jammer is judged by its range. Jammers with a higher range can block signals of devices that are present upto that range. Fig. 6 shows the implementation of the jamming attack. The attacker sends the jamming signal to the serving base station from his end, with the help of a UAV, which matches the frequency of the signal with the deployed drone. Thereafter, the signals between the drone and the backup serving base station are blocked. Hence, no data and commands ...
Context 2
... in the jamming attack radio signals are used to attack the drones which mainly affect the physical layer. Signals of Wi-Fi and Bluetooth can be easily jammed, and that too using a low power jammer [42]. The ability of the jammer is judged by its range. Jammers with a higher range can block signals of devices that are present upto that range. Fig. 6 shows the implementation of the jamming attack. The attacker sends the jamming signal to the serving base station from his end, with the help of a UAV, which matches the frequency of the signal with the deployed drone. Thereafter, the signals between the drone and the backup serving base station are blocked. Hence, no data and commands ...
Citations
... Although basic communication facilities can basically satisfy the daily communication load, however, when unexpected situations arise or in unconventional temporary scenarios, relying only on terrestrial wireless communication facilities becomes insufficient [16], such as network reconstruction in major natural disasters, temporary communication deployment in remote areas, and wireless resource allocation at the scene of major holiday gatherings [17]. In order to effectively improve the quality of wireless communication in these scenarios, drones can be deployed to assist communication [18]. As a small aircraft, UAVs play an important role in the field of communication. ...
With the advancement of wireless communication technology, the number of wireless network terminals has exploded, and various new business scenarios have emerged. The 6G mobile communication technology not only surpasses 5G standards in terms of transmission rate, delay, power and other performances, but also extends the communication range to multiple fields such as air, ground, ocean, etc., which greatly promotes Unmanned Aerial Vehicle (UAV) communication technology research and development. Compared to terrestrial networks, UAV communication has advantages such as high flexibility and easy deployment. However, there are still many problems and challenges in practical applications. In this paper, we will first introduce the functions and application scenarios of UAV communication, then discuss the current challenges and related technical research, and finally look forward to the future development prospects.
... To address the problem, the electromagnetic interference information service of U-space has been planned for phase U2 [17]. Other common attacks on drone communication links are denial of service, de-authentication attacks, man-in-the-middle attacks, trojans, etc., discussed in detail in [71]. The possible solution to mitigate these attacks, or at least to tackle them, could be the use of a machine learning approach for the identification of legitimate drones and, in addition, a common database [72], deep learning algorithms for the routing protocols [73], and blockchain technology for the encryption and decryption of secure information [74]. ...
This paper identifies and classifies the essential constraints that must be addressed to allow U-space traffic autonomous guidance. Based on an extensive analysis of the state of the art in robotic guidance, physics of flight, flight safety, communication and navigation, uncrewed aircraft missions, artificial intelligence (AI), social expectations in Europe on drones, etc., we analyzed the existing constraints and the information needs that are of essential importance to address the identified constraints. We compared the identified information needs with the last edition of the U-space Concept of Operations and identified critical gaps between the needs and proposed services. A high-level methodology to identify, measure, and close the gaps is proposed.
... Communication between drones and ground control systems involves receiving commands, transmitting telemetry data, and establishing a reliable control link. This interaction is typically facilitated through dedicated datalinks, such as radio frequency (RF) or wireless communication protocols [5]. The central control point for monitoring and managing multiple drones concurrently is the ground control system (GCS) [6]. ...
... The central control point for monitoring and managing multiple drones concurrently is the ground control system (GCS) [6]. Additionally, drones rely heavily on navigation satellite systems like GPS to precisely determine their location, altitude, and velocity [5,6]. Moreover, drones operating within controlled airspace adhere to communication requirements with air traffic control systems, ensuring safe integration, coordination with other aircraft, and regulation compliance. ...
With the rapid proliferation of drones and drone networks across various application domains, ensuring their security against cyber threats has become imperative. This paper presents a comprehensive analysis and comparative analysis of the state-of-the-art techniques for detecting cyber threats in drone networks. The background provides a primer on drones, networks, drone network architectures, communication mechanisms, and enabling technologies like wireless protocols, satellite navigation, onboard computers, sensors, and flight control systems. The landscape of emerging technologies including blockchain, software-defined networking, machine learning, fog computing, ad-hoc networks, and swarm intelligence is reviewed in the context of transforming drone network capabilities while also introducing potential vulnerabilities. The paper delves into common cyber threats faced by drone networks such as hacking, DoS attacks, data breaches, and GPS spoofing. A detailed literature review of proposed threat detection techniques is provided, categorized into machine learning, multi-agent systems, blockchain, intrusion detection systems, software solutions, and miscellaneous methods. A key gap identified is handling increasingly sophisticated attacks, complex environments, and resource limitations in aerial platforms. The analysis highlights accuracy, overhead and real-time trade-offs between techniques, while factors like model optimization can influence efficacy. A comparative analysis highlights the advantages and limitations of each approach considering metrics like accuracy, scalability, flexibility, and overhead. Key observations include the trade-offs between computational complexity and real-time performance, the challenges in handling evolving attack techniques, and the dependencies between detection accuracy and factors like model selection and training data quality. The analysis provides a comprehensive reference for cyber threat detection in drone networks, benefiting researchers and practitioners aiming to advance this crucial area of drone security through robust detection systems tailored for resource-constrained aerial environments.
... Security [2,3,[7][8][9][10][11][12]14,15,22,24,. ...
... While numerous survey papers have extensively covered the security and privacy aspects of UAV networks [12,[84][85][86], there is a distinct need for a comprehensive overview addressing reputation systems within UAV networks. Our paper fills this gap by exclusively focusing on the mechanisms and frameworks that establish and maintain trust and reputation in UAV networks. ...
The proliferation of unmanned aerial vehicle (UAV) networks is increasing, driven by their capacity to deliver automated services tailored to the varied demands of numerous smart city applications. Trust, security, and privacy remain paramount in the public domain. Traditional centralized network designs fall short of ensuring device authentication, data integrity, and privacy within the highly dynamic and adaptable environments of UAV networks. Decentralized reputation systems have emerged as a promising solution for enhancing the reliability and trustworthiness of data and communications within these networks while safeguarding UAV security. This paper presents an exhaustive survey of trust and reputation systems, exploring existing frameworks and proposed innovations alongside their inherent challenges. The crucial role of reputation systems is to strengthen trust, security, and privacy throughout these networks, and various strategies can be incorporated to mitigate existing vulnerabilities. As a useful resource for researchers and practitioners seeking to advance the state of the art in UAV network security, we hope this survey will spark further community discussion and stimulate innovative ideas in this burgeoning field.
... For safety-critical UAVs operating in real-world environments, their vulnerabilities could potentially lead to severe accidents. For example, the openness of communication networks, such as GPS, as shown in Fig. 1, makes UAVs particularly susceptible to cyber attacks [5] [6], and these challenges have already turned into reality, causing significant damages and even tragedies [7]- [12]. In [7] and [8], the authors employ both overt and covert spoofing strategies to manipulate GPS measurements, successfully capturing and controlling the Hornet Mini UAV, leading to irrecoverable errors and its subsequent crash. ...
Safety-critical intelligent cyber-physical systems, such as quadrotor unmanned aerial vehicles (UAVs), are vulnerable to different types of cyber attacks, and the absence of timely and accurate attack detection can lead to severe consequences. When UAVs are engaged in large outdoor maneuvering flights, their system constitutes highly nonlinear dynamics that include non-Gaussian noises. Therefore, the commonly employed traditional statistics-based and emerging learning-based attack detection methods do not yield satisfactory results. In response to the above challenges, we propose QUADFormer, a novel Quadrotor UAV Attack Detection framework with transFormer-based architecture. This framework includes a residue generator designed to generate a residue sequence sensitive to anomalies. Subsequently, this sequence is fed into a transformer structure with disparity in correlation to specifically learn its statistical characteristics for the purpose of classification and attack detection. Finally, we design an alert module to ensure the safe execution of tasks by UAVs under attack conditions. We conduct extensive simulations and real-world experiments, and the results show that our method has achieved superior detection performance compared with many state-of-the-art methods.
... Through the creation of drone models and a thorough understanding of their surroundings, coupled with the integration of a reliable physics engine and the successful resolution of considerable design obstacles we have created a platform that provides insights, into the dynamics of drone swarms [12]. This system serves as a valuable testing ground for real-world applications, across diverse fields. ...
The rapid evolution of unmanned aerial vehicles (UAVs), has significantly advanced their capabilities, enabling complex operations that can be enhanced through swarm intelligence. This paper introduces a drone swarm simulator designed to model, analyze, and optimize the cooperative behaviors of drone swarms in diverse operational environments to provide a realistic and scalable platform for the simulation of drones, incorporating real-world physics, communication constraints, and autonomous decision-making algorithms.
... Moreover, Refs. [42,43] describe enabling technologies of IoD, while mentioning blockchain as well. In [44], the authors propose the combination of 5G networks and blockchain by examining security issues, while in [45] the authors discuss AI and blockchain as underpinning technologies, including use cases of the IoD and blockchain. ...
The Internet of Drones (IoD) is a decentralized network linking drones’ access to controlled airspace, providing high adaptability to complex scenarios and services to various drone applications, such as package delivery, traffic surveillance, and rescue, including navigation services. Unmanned Aerial Vehicles (UAVs), combined with IoD principles, offer numerous strengths, e.g., high mobility, wireless coverage areas, and the ability to reach inaccessible locations, including significant improvements such as reliability, connectivity, throughput, and decreased delay. Additionally, emerging blockchain solutions integrated within the concept of the IoD enable effective outcomes that surpass traditional security approaches, while enabling decentralized features for smart human-centered applications. Nevertheless, the combination of the IoD and blockchain faces many challenges with emerging open issues that require further investigation. In this work, we thoroughly survey the technological concept of the IoD and fundamental aspects of blockchain, while investigating its contribution to current IoD practices, the impact of novel enabling technologies, and their active role in the combination of the corresponding synergy. Moreover, we promote the combination of the two technologies by researching their collaborative functionality through different use cases and application fields that implement decentralized IoD solutions and highlighting their indicative benefits, while discussing important challenges and future directions on open issues.
... This data link is essential for timely communication and pivotal for delaysensitive tasks. UAVs use radio waves to communicate, which allows them to send and receive data in places that are either difficult to access or hazardous for human operators [10]. ...
The proliferation of Unmanned Aerial Vehicle (UAV) networks is increasing, driven by their capacity to deliver automated services tailored to the varied demands of numerous smart city applications. Trust, security, and privacy remain paramount in the public domain. Traditional centralized network designs fall short of ensuring device authentication, data integrity, and privacy within UAV networks’ highly dynamic and adaptable environments. Decentralized reputation systems emerge as a promising solution to enhance the reliability and trustworthiness of data and communications within these networks while safeguarding UAV security. This paper presents an exhaustive survey of trust and reputation systems, exploring existing frameworks and proposed innovations alongside their inherent challenges. It highlights the crucial role of reputation systems in strengthening trust, security, and privacy throughout these networks and discusses various strategies to mitigate existing vulnerabilities. As a useful resource for researchers and practitioners seeking to advance the state of the art in UAV network security, we hope this survey will spark further community discussion and stimulate innovative ideas in this burgeoning field.
... In the forthcoming years, the use of autonomous and intelligent drones is expected to rise exponentially. To ensure secure flight and mitigate the potential risks associated with drones, the development of drone-to-drone detection technology assumes paramount significance [7][8][9]. Notably, this research domain remains largely unexplored, offering ample opportunities for investigation and advancement. ...
With the continuous advancement of drone technology, drones are demonstrating a trend toward autonomy and clustering. The detection of airborne objects from the perspective of drones is critical for addressing threats posed by aerial targets and ensuring the safety of drones in the flight process. Despite the rapid advancements in general object detection technology in recent years, the task of object detection from the unique perspective of drones remains a formidable challenge. In order to tackle this issue, our research presents a novel and efficient mechanism for adjacent frame fusion to enhance the performance of visual object detection in airborne scenarios. The proposed mechanism primarily consists of two modules: a feature alignment fusion module and a background subtraction module. The feature alignment fusion module aims to fuse features from aligned adjacent frames and key frames based on their similarity weights. The background subtraction module is designed to compute the difference between the foreground features extracted from the key frame and the background features obtained from the adjacent frames. This process enables a more effective enhancement of the target features. Given that this method can significantly enhance performance without a substantial increase in parameters and computational complexity, by effectively leveraging the feature information from adjacent frames, we refer to it as an efficient adjacent frame fusion mechanism. Experiments conducted on two challenging datasets demonstrate that the proposed method achieves superior performance compared to existing algorithms.
... The delivery may be disrupted by hackers who intercept data, spoof GPS signals, inject malware/ malicious code, or even take over drones ( Kim et al., 2012;Ly & Ly, 2021;Tang, 2021 ). To mitigate the risk of cyber-attacks, both drone manufacturers and operators should propose technological solutions and policies, including authentication, access control mechanisms, strong encryption, privacy preservation, protocol improvement and hardware / software updates ( Hassija et al., 2021;Sanjab et al., 2017;Yang et al., 2022 ). ...
The application of drone technology promises to revolutionize the transportation industry. Particularly, the combination of drones with ground vehicles has tremendous advantages for delivery applications, including an increased delivery speed and reduced operating costs, while keeping drones lightweight and small. Accordingly, the number of research studies targeting the Coordinated Delivery of Trucks and Drones (CDTD) has increased significantly in the past decade. Most of these existing studies, however, have put a strong emphasis on the optimization aspects, usually by solving combinatorial problems induced by the delivery coordination and the goal to minimize a specific objective function. Here, we contribute to the extant body of literature by providing a comprehensive review and discussion of policy-related challenges for a successful CDTD implementation. Given that various industry stakeholders, e.g., Amazon, Uber, and SF express, are already in the process of pushing the envelope for CDTD operations, we believe that our contribution is timely and complementary, helping policy makers to make informed decision regarding the support and regulation of this new technology.
