Jamming Detection and Classification via Conventional Machine Learning and Deep Learning with Applications to UAVs

With the constant advancement of modern radio technology, the safety of radio communication has become a growing concern for us. Communication has become an essential component, particularly in the application of modern technology such as unmanned aerial vehicle (UAV). As a result, it is critical to ensure that a drone can fly safely and reliably while completing duties. Simultaneously, machine learning (ML) is rapidly developing in the twenty-first century. For example, ML is currently being used in social media and digital marking for predicting and addressing users' varies interests. This also serves as the impetus for this thesis. The goal of this thesis is to combine ML and radio communication to identify and classify UAV interference with high accuracy.

In this work, a ML approach is explored for detecting and classifying jamming attacks against orthogonal frequency division multiplexing (OFDM) receivers, with applicability to UAVs. Four types of jamming attacks, including barrage, protocol-aware, single-tone, and successive-pulse jamming, are launched and analyzed using software-defined radio (SDR). The jamming range, launch complexity, and attack severity are all considered qualitatively when evaluating each type. Then, a systematic testing procedure is established, where a SDR is placed in the vicinity of a drone to extract radiometric features before and after a jamming attack is launched. Traditional ML methods are used to create classification models with numerical features such as signal-to-noise ratio (SNR), energy threshold, and important OFDM parameters. Furthermore, deep learning method (i.e., convolutional neural networks) are used to develop classification models trained with spectrogram images filling in it. Quantitative indicators such as detection and false alarm rates are used to evaluate the performance of both methods. The spectrogram-based model correctly classifies jamming with a precision of 99.79% and a false-alarm rate of 0.03%, compared to 92.20% and 1.35% for the feature-based counterpart.