Detection of LSSUAV using hash fingerprint based SVDD

Shi, Zhiyuan; Huang, Minmin; Zhao, Caidan; Huang, Lianfen; Du, Xiaojiang; Zhao, Yifeng

doi:10.1109/icc.2017.7996844

Cited by 26 publications

(14 citation statements)

References 17 publications

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“…They explore algorithms to scan known radio frequencies, so as to find and geolocate RF-emitting drones, despite weather and day/night conditions. Many studies have used RF scanners, either for locating a drone in space or for classifying FPV (first-persons view) channel transmissions [ 5 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ]. Nguyen et al [ 56 , 57 , 58 ] analyzed RF signals captured by software-defined radio (SDR) in order to detect commercial UAVs with high accuracy from a distance of up to 600 m. UAVs were traced with variable accuracy (64–89%), depending on the drone type.…”

Section: Literature Review On Counter-drone (C-uas) Technologiesmentioning

confidence: 99%

“…Scheller [ 59 ] investigated drone detection in heavy-RF environments, where an RF drone’s signature at a distance more than 100 m away could not be observed. Two studies [ 60 , 61 ] classified UAVs by using machine learning algorithms, while Peacock [ 61 ] proposed the analysis of UAV’s MAC address in order to detect and deactivate specific UAVs. Nevertheless, it is obvious that attackers can change a drone’s MAC address to avoid identification.…”

Section: Literature Review On Counter-drone (C-uas) Technologiesmentioning

confidence: 99%

See 1 more Smart Citation

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

Lykou

Moustakas

Gritzalis

2020

Sensors

163

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As the fastest growing segment of aviation, unmanned aerial systems (UAS) continue to increase in number, technical complexity and capabilities. Numerous civilian and commercial uses are drastically transforming civil protection, asset delivery, commercial and entertaining activities. However, UAS pose significant challenges in terms of safety, security and privacy within society. An increasing phenomenon, nowadays, is drone-related incidents near airport facilities, which are expected to proliferate in frequency, complexity and severity, as drones become larger and more powerful. Critical infrastructures need to be protected from such aerial attacks, through effective counteracting technologies, risk management and resilience plans. In this paper, we present a survey of drone incidents near airports and a literature review of sensor technologies, able to prevent, detect, identify and mitigate rogue drones. We exhibit the benefits and limitations of available counter-drone technologies (C-UAS); however, defending airports against misused drone activity is a hard problem. Therefore, we analyze three realistic attack scenarios from malicious drones and propose an effective C-UAS protection plan for each case. We discuss applicability limitations of C-UAS in the aviation context and propose a resilience action plan for airport stakeholders for defending against airborne threats from misused drones.

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Section: Literature Review On Counter-drone (C-uas) Technologiesmentioning

confidence: 99%

Section: Literature Review On Counter-drone (C-uas) Technologiesmentioning

confidence: 99%

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

Lykou

Moustakas

Gritzalis

2020

Sensors

163

View full text Add to dashboard Cite

show abstract

“…The security authentication based on physical layer characteristics of signals shows advantages [80,81] . On the one hand, it reduces the calculation and communication overhead of the authentication process without strong calculation capabilities.…”

Section: Uav-aided High Accuracy Vehicular Positioningmentioning

confidence: 99%

Space-air-ground integrated vehicular network for connected and automated vehicles: Challenges and solutions

Niu

Shen

Zhang

et al. 2020

Intell. and Converged Netw.

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“…Zhang et al proposed a fault diagnosis framework of analog circuits based on a single classifier, and introduced "or" combined results into the test samples to solve the fault in the overlapping region of test samples [42]. In recent years, some research on SVDD algorithm has been published [43,44], and further reasonable improvements have been made to the SVDD algorithm.…”

Section: Support Vector Data Descriptionmentioning

confidence: 99%

An Identity Authentication Method of a MIoT Device Based on Radio Frequency (RF) Fingerprint Technology

Tian

Lin

Guo

et al. 2020

Sensors

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With the continuous development of science and engineering technology, our society has entered the era of the mobile Internet of Things (MIoT). MIoT refers to the combination of advanced manufacturing technologies with the Internet of Things (IoT) to create a flexible digital manufacturing ecosystem. The wireless communication technology in the Internet of Things is a bridge between mobile devices. Therefore, the introduction of machine learning (ML) algorithms into MIoT wireless communication has become a research direction of concern. However, the traditional key-based wireless communication method demonstrates security problems and cannot meet the security requirements of the MIoT. Based on the research on the communication of the physical layer and the support vector data description (SVDD) algorithm, this paper establishes a radio frequency fingerprint (RFF or RF fingerprint) authentication model for a communication device. The communication device in the MIoT is accurately and efficiently identified by extracting the radio frequency fingerprint of the communication signal. In the simulation experiment, this paper introduces the neighborhood component analysis (NCA) method and the SVDD method to establish a communication device authentication model. At a signal-to-noise ratio (SNR) of 15 dB, the authentic devices authentication success rate (ASR) and the rogue devices detection success rate (RSR) are both 90%.

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Detection of LSSUAV using hash fingerprint based SVDD

Cited by 26 publications

References 17 publications

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

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

Space-air-ground integrated vehicular network for connected and automated vehicles: Challenges and solutions

An Identity Authentication Method of a MIoT Device Based on Radio Frequency (RF) Fingerprint Technology

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