{'fraction_non_alphanumeric': 0.0311284046692607, 'fraction_numerical': 0.0023346303501945525, 'mean_word_length': 5.123809523809523, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Drone Detection and Classification Using Cellular Network: A Machine Learning Approach

The main target of this paper is to propose a preferred set of features from a cellular network for using as predictors to do the classification between the rogue flying drone User Equipments (UEs) and regular UEs for different Machine Learning (ML) models. Furthermore, the target is to study four different machine learning models i.e. Decision Tree (DT), Logistic Regression (LR). Discriminant Analysis (DA) and K-Nearest Neighbour (KNN) in this paper, and evaluate/compare their performance in terms of identifying the flying drone UE using three performance metrics i.e. True Positive Rate (TPR), False Positive Rate (FPR) and area under Receiver Operating Characteristic (ROC) curve. The simulations are performed using an agreed 3GPP scenario, and a MATLAB machine learning tool box. All considered ML models provide high drone detection probability for drones flying at 60 m and above height. However, the true drone detection probability degrades for drones at lower altitude. Whereas, the fine DT method and the coarse KNN model performs relatively better compared with LR and DA at low altitude, and therefore can be considered as a preferable choice for a drone classification problem.