  The number of end devices that use the last mile wireless connectivity is
dramatically increasing with the rise of smart infrastructures and require
reliable functioning to support smooth and efficient business processes. To
efficiently manage such massive wireless networks, more advanced and accurate
network monitoring and malfunction detection solutions are required. In this
paper, we perform a first time analysis of image-based representation
techniques for wireless anomaly detection using recurrence plots and Gramian
angular fields and propose a new deep learning architecture enabling accurate
anomaly detection. We examine the relative performance of the proposed model
and show that the image transformation of time series improves the performance
of anomaly detection by up to 29% for binary classification and by up to 27%
for multiclass classification. At the same time, the best performing model
based on recurrence plot transformation leads to up to 55% increase compared to
the state of the art where classical machine learning techniques are used. We
also provide insights for the decisions of the classifier using an instance
based approach enabled by insights into guided back-propagation. Our results
demonstrate the potential of transformation of time series signals to images to
improve classification performance compared to classification on raw time
series data.
