Security in Internet of Drones: A Comprehensive Review

Samanth, Snehal; Prema, K. V.; Balachandra, Mamatha

doi:10.1080/23311916.2022.2029080

“…

where

= area of interest/the mapped area priority level, and

= authorization permit/authentication key to operate within such a classified zone. Authenticating a drone’s authorization status in a DTN is an emerging research issue [ 28 , 29 , 30 ].…”

Section: Methodsmentioning

confidence: 99%

ALIEN: Assisted Learning Invasive Encroachment Neutralization for Secured Drone Transportation System

Ajakwe

¹

,

Ihekoronye

²

,

Kim

³

et al. 2023

Sensors

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Priority-based logistics and the polarization of drones in civil aviation will cause an extraordinary disturbance in the ecosystem of future airborne intelligent transportation networks. A dynamic invention needs dynamic sophistication for sustainability and security to prevent abusive use. Trustworthy and dependable designs can provide accurate risk assessment of autonomous aerial vehicles. Using deep neural networks and related technologies, this study proposes an artificial intelligence (AI) collaborative surveillance strategy for identifying, verifying, validating, and responding to malicious use of drones in a drone transportation network. The dataset for simulation consists of 3600 samples of 9 distinct conveyed objects and 7200 samples of the visioDECT dataset obtained from 6 different drone types flown under 3 different climatic circumstances (evening, cloudy, and sunny) at different locations, altitudes, and distance. The ALIEN model clearly demonstrates high rationality across all metrics, with an F1-score of 99.8%, efficiency with the lowest noise/error value of 0.037, throughput of 16.4 Gbps, latency of 0.021, and reliability of 99.9% better than other SOTA models, making it a suitable, proactive, and real-time avionic vehicular technology enabler for sustainable and secured DTS.

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“…A specific feature of the scalar product calculation operation used to decrypt encrypted data is that the weights are pre-calculated (constants) and set in floating-point format, while the encrypted data 𝑦𝑦 𝑗𝑗 are received in block-floating-point format. The calculation of the scalar product using the tabular-algorithmic method is performed by formula (7). Preparation and calculation of possible variants of macro-partial products is performed as in the previous case under the formula (8).…”

Section: Xormentioning

confidence: 99%

“…A key problem is to guarantee cryptographic security of data transmission in the management of UAV [1,2], intelligent robots [3], microsatellites [4] and various mobile transport systems [5]. Due to the security vulnerabilities of UAVs, and illegal and malicious attacks against UAVs, especially against communication data and UAV control, solutions to prevent such attacks are needed, and one of them is to encrypt UAV's communication data [6,7,8]. Due to limited battery capacity, Unmanned Aerial Vehicles (UAVs) must use energy-efficient data processing [9].…”

Section: Introductionmentioning

confidence: 99%

Neural Network Technology for Cryptographic Protection of Data Transmission at UAV

Tsmots¹,

Teslyuk²,

Łukaszewicz³

et al. 2023

Preprint

0

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The neural network technology for real-time cryptographic data protection with symmetric keys (masking codes, neural network architecture and weights matrix) for unmanned aerial vehicles (UAV) onboard communication systems has been developed. It provides hardware and software implementation with high technical and operational characteristics. The development of neural network technology for real-time cryptographic data protection was performed using an integrated approach based on theoretical foundations of neural network cryptographic data protection, new algorithms and structures of neural network data encryption and decryption, modern element base with the possibility of programming for the structure, and computer-aided design of hardware and software tools. The development and implementation of the on-board system of neural network cryptographic data protection in real-time are based on the following principles: variable composition of equipment; modularity; conveyorization and spatial parallelism; specialization and adaptation of hardware and software to data encryption and decryption. The tabular-algorithmic method of calculating the scalar product has been improved, it provides fast calculation of the scalar product of input data for both fixed and floating-point by bringing to the largest common order of weights and forming tables of macro-partial products for them. Components of neural network cryptographic data encryption and decryption have been developed on the processor core supplemented by specialized scalar product calculation modules. The specialized hardware for neural network cryptographic data encryption was developed using VHDL for equipment programming in the Quartus II development environment ver. 13.1 and the appropriate libraries, and implemented on the basis of the FPGA EP3C16F484C6 Cyclone III family.

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“…A key problem is to guarantee cryptographic security of data transmission in the management of UAV [1,2], intelligent robots [3], microsatellites [4] and various mobile transport systems [5]. Due to the security vulnerabilities of UAVs, and illegal and malicious attacks against UAVs, especially against communication data and UAV control, solutions to prevent such attacks are needed, and one of them is to encrypt UAV's communication data [6,7,8]. Unmanned Aerial Vehicles (UAVs) must be energy-efficient, especially in data processing, because of limited battery capacity [9].…”

Section: Introductionmentioning

confidence: 99%

Neural Network Technology for Cryptographic Protection of Data Transmission at UAV

Tsmots¹,

Teslyuk²,

Łukaszewicz³

et al. 2023

Preprint

1

0

View full text Add to dashboard Cite

The neural network technology for real-time cryptographic data protection with symmetric keys (masking codes, neural network architecture and weights matrix) for unmanned aerial vehicles (UAV) onboard communication systems has been developed. It provides hardware and software implementation with high technical and operational characteristics. The development of neural network technology for real-time cryptographic data protection was performed using an integrated approach based on theoretical foundations of neural network cryptographic data protection, new algorithms and structures of neural network data encryption and decryption, modern element base with the possibility of programming for the structure, and computer-aided design of hardware and software tools. The development and implementation of the on-board system of neural network cryptographic data protection in real-time are based on the following principles: variable composition of equipment; modularity; conveyorization and spatial parallelism; specialization and adaptation of hardware and software to data encryption and decryption. The tabular-algorithmic method of calculating the scalar product has been improved, it provides fast calculation of the scalar product of input data for both fixed and floating-point by bringing to the largest common order of weights and forming tables of macro-partial products for them. Components of neural network cryptographic data encryption and decryption have been developed on the processor core supplemented by specialized scalar product calculation modules. The specialized hardware for neural network cryptographic data encryption was developed using VHDL for equipment programming in the Quartus II development environment ver. 13.1 and the appropriate libraries, and implemented on the basis of the FPGA EP3C16F484C6 Cyclone III family.

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Security in Internet of Drones: A Comprehensive Review

Cited by 26 publications

References 200 publications

ALIEN: Assisted Learning Invasive Encroachment Neutralization for Secured Drone Transportation System

ALIEN: Assisted Learning Invasive Encroachment Neutralization for Secured Drone Transportation System

Neural Network Technology for Cryptographic Protection of Data Transmission at UAV

Neural Network Technology for Cryptographic Protection of Data Transmission at UAV

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