A Deep Learning Model for Anomalous Wireless Link Detection

Machine learning (ML) techniques play a significant role in detecting anomalous wireless links. However, to date, to the extent of our knowledge, there is no robust classifier that would work in a realistic scenario where various anomalies could appear concurrently in the time-series gleaned from the network monitoring tools. In this paper, we propose a new deep learning based classifier and show that is able to outperform the state of the art for existing link layer anomalies. Our evaluation results demonstrate that the state-of-the ML models perform with an average accuracy of about 63%, whereas the average accuracy of the proposed DL model is around 90%, indicating a significant improvement of 27 percentage points anomaly detection performance.