Bi-Scale Mahalanobis Detection for Reactive Jamming in UAV OFDM Links

Nassim Aich, Zakarya Oubrahim, Hachem Ait Talount and Ahmed Abbou ()
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Nassim Aich: EREEC Laboratory, Department of Electrical Engineering, Mohammadia School of Engineers, Mohammed V University, Rabat 10000, Morocco
Zakarya Oubrahim: EREEC Laboratory, Department of Electrical Engineering, Mohammadia School of Engineers, Mohammed V University, Rabat 10000, Morocco
Hachem Ait Talount: EREEC Laboratory, Department of Electrical Engineering, Mohammadia School of Engineers, Mohammed V University, Rabat 10000, Morocco
Ahmed Abbou: EREEC Laboratory, Department of Electrical Engineering, Mohammadia School of Engineers, Mohammed V University, Rabat 10000, Morocco

Future Internet, 2025, vol. 17, issue 10, 1-26

Abstract: Reactive jamming remains a critical threat to low-latency telemetry of Unmanned Aerial Vehicles (UAVs) using Orthogonal Frequency Division Multiplexing (OFDM). In this paper, a Bi-scale Mahalanobis approach is proposed to detect and classify reactive jamming attacks on UAVs; it jointly exploits window-level energy and the Sevcik fractal dimension and employs self-adapting thresholds to detect any drift in additive white Gaussian noise (AWGN), fading effects, or Radio Frequency (RF) gain. The simulations were conducted on 5000 frames of OFDM signals, which were distorted by Rayleigh fading, a ±10 kHz frequency drift, and log-normal power shadowing. The simulation results achieved a precision of 99.4%, a recall of 100%, an F1 score of 99.7%, an area under the receiver operating characteristic curve (AUC) of 0.9997, and a mean alarm latency of 80 μs. The method used reinforces jam resilience in low-power commercial UAVs, yet it needs no extra RF hardware and avoids heavy deep learning computation.

Keywords: unmanned aerial vehicles; reactive jamming detection; fractal dimension; Mahalanobis distance; RMS energy (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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