A Machine Learning Approach for Detecting and Classifying Jamming Attacks Against OFDM-based UAVs
Jered Pawlak, Yuchen Li, Joshua Price, Matthew Wright, Khair Al Shamaileh, Quamar Niyaz, Vijay Devabhaktuni
Abstract
In this paper, a machine learning (ML) approach is proposed to detect and classify jamming attacks on unmanned aerial vehicles (UAVs). Four attack types are implemented using software-defined radio (SDR); namely, barrage, single-tone, successive-pulse, and protocol-aware jamming. Each type is launched against a drone that uses orthogonal frequency division multiplexing (OFDM) communication to qualitatively analyze its impacts considering jamming range, complexity, and severity. Then, an SDR is utilized in proximity to the drone and in systematic testing scenarios to record the radiometric parameters before and after each attack is launched. Signal-to-noise ratio (SNR), energy threshold, and several OFDM parameters are exploited as features and fed to six ML algorithms to explore and enable autonomous jamming detection/classification. The algorithms are quantitatively evaluated with metrics including detection and false alarm rates to evaluate the received signals and facilitate efficient decision-making for improved reception integrity and reliability. The resulting ML approach detects and classifies jamming with an accuracy of 92.2% and a false-alarm rate of 1.35%.