Drone Cyber-Attack: An Intrusion Detection Technique Based on RSSI and Trilateration

Chibi, Nabil Tazi; Ghazi, Hassan El; Fihri, Wassim Fassi

doi:10.1109/tst52996.2021.00014

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…A ground controller station can also be compromised by the adversary, which will then send a suspicious signal to mislead a drone or even cause a crash. Through an analysis of the receiver signal strength indicator (RSSI) values and by triangulating the numbers with neighboring drones and their data, the scheme proposed in [11] can prove to be resilient to DoS attacks.…”

Section: Denial Of Servicementioning

confidence: 99%

Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning

2022

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Drones are increasingly adopted to serve a smart city through their ability to render quick and adaptive services. They are also known as unmanned aerial vehicles (UAVs) and are deployed to conduct area surveillance, monitor road networks for traffic, deliver goods and observe environmental phenomena. Cyber threats posed through compromised drones contribute to sabotage in a smart city’s airspace, can prove to be catastrophic to its operations, and can also cause fatalities. In this contribution, we propose a machine learning-based approach for detecting hijacking, GPS signal jamming and denial of service (DoS) attacks that can be carried out against a drone. A detailed machine learning-based classification of drone datasets for the DJI Phantom 4 model, compromising both normal and malicious signatures, is conducted, and results obtained yield advisory to foster futuristic opportunities to safeguard a drone system against such cyber threats.

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Section: Denial Of Servicementioning

confidence: 99%

Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning

2022

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“…However, in this system, no WPT mechanism is used at all. The RSSI system described previously [7] is used to detect and issue warnings to drones regarding the authenticity of the received signals with a focus on programmed calculations for achieving its objectives, and no description of a circuit design for RSSI implementation is provided. An in-depth analysis of an RSSI system for a wireless network is presented in [8], functioning at 30 KHz whilst receiving a singletone sinusoidal waveform.…”

Section: Introductionmentioning

confidence: 99%

Linear-in-dB Logarithmic Signal Strength Sensor Circuit for Wireless Power Transfer Receivers

Quadir,

Alawar,

Albasha

et al. 2023

Energies

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Tracking systems for wireless power transfer are becoming a necessity. The received target signals are sometimes weak, which is why it is critical to have a dedicated received–signal–strength indicator (RSSI) for signal detection. It can also be used for transmitter localization and automatic gain control (AGC) to ensure continuous coverage. In this paper, a logarithmic detector coupled with a high-speed full-wave rectifier is designed for an RSSI system. The aim is to place the RSSI on the receiver side of unmanned aerial vehicles used, for example, as agricultural drones. This system is intended to operate in a scenario where multiple drones in a smart farm are charged wirelessly whilst airborne. An RSSI is placed in the receiver to detect and track wireless signals and to ensure the drone is charged while in motion. The RSSI system operates at 5.8 GHz, reported for the first time, and is capable of detecting signal strengths from −60 dBm to 0 dBm with a sensitivity level of 17 mV/dBm. A logarithmic error of 0.4 dB with a dynamic range of 34 dB was achieved. The proposed RSSI system was designed using 65 nm TSMC CMOS technology, and it exhibits high sensitivity, better efficiency, and lower power consumption than those in other reported works.

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“…However, it was found that if the path loss exponent n is a piecewise function of distance, ambiguity will occur, and moreover, if the path loss is not a monotonic function of distance, the method presented in [8] cannot be used. The authors of [9] proposed locating an RF source using the RSSI and trilateration. However, their proposed method requires the path loss exponent n and the power that will be received at a one-meter distance from the RF source.…”

Section: Introductionmentioning

confidence: 99%

Drone Controller Localization Based on RSSI Ratio

Wang

Zeng

Sun

et al. 2023

Sensors

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We proposed two methods for the localization of drone controllers based on received signal strength indicator (RSSI) ratios: the RSSI ratio fingerprint method and the model-based RSSI ratio algorithm. To evaluate the performance of our proposed algorithms, we conducted both simulations and field trials. The simulation results show that our two proposed RSSI-ratio-based localization methods outperformed the distance mapping algorithm proposed in literature when tested in a WLAN channel. Moreover, increasing the number of sensors improved the localization performance. Averaging a number of RSSI ratio samples also improved the performance in propagation channels that did not exhibit location-dependent fading effects. However, in channels with location-dependent fading effects, averaging a number of RSSI ratio samples did not significantly improve the localization performance. Additionally, reducing the grid size improved the performance in channels with small shadowing factor values, but this only resulted in marginal gains in channels with larger shadowing factors. Our field trial results align with the simulation results in a two-ray ground reflection (TRGR) channel. Our methods provide a robust and effective solution for the localization of drone controllers using RSSI ratios.

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Drone Cyber-Attack: An Intrusion Detection Technique Based on RSSI and Trilateration

Cited by 4 publications

References 10 publications

Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning

Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning

Linear-in-dB Logarithmic Signal Strength Sensor Circuit for Wireless Power Transfer Receivers

Drone Controller Localization Based on RSSI Ratio

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