Machine Learning-Based Model Selection for Anomalous Wireless Link Detection

Machine learning (ML) techniques play a significant role in detecting abnormal link operations in wireless networks. However, during the design and development process of a ML-based model, numerous factors including input data transformation, scaling methods, choice of ML techniques and their associated parametrization, influence the final model performance. In this paper, we discuss the design and development process of wireless link anomaly detection models followed by the model ranking process for selecting the most suitable models given the required application criteria. Under this premise, we consider four different data representations, four anomaly types, six scaling methods and six distinct ML techniques along with their associated parametrization trials leading to over twenty thousand anomaly detection models for wireless links. We show that the most suitable supervised ML models are able to detect anomalies with an F1 score of at least 88% and up to perfect score, whereas unsupervised ML models can attain an F1 score of up to 96% depending on the anomaly type, and on average, amongst 20,352 available models, the supervised SVM model can be readily adopted to efficiently detect any type of anomaly with an F1 score of at least 94%, particularly when trained on encoded data representations using the combination of "mean and deviation scaling" and "non-linear RBF kernel".