A Testbed for Automating and Analysing Mobile Devices and their Applications
This addresses the problem of improving network situational awareness for administrators by providing tools to develop machine learning techniques, though it is incremental as it builds on existing testbed concepts.
The authors tackled the lack of realistic labeled network traffic datasets for mobile devices by developing a testbed that automates applications to generate and label such traffic, resulting in two labeled datasets and benchmarks for application classification.
The need for improved network situational awareness has been highlighted by the growing complexity and severity of cyber-attacks. Mobile phones pose a significant risk to network situational awareness due to their dynamic behaviour and lack of visibility on a network. Machine learning techniques enhance situational awareness by providing administrators insight into the devices and activities which form their network. Developing machine learning techniques for situational awareness requires a testbed to generate and label network traffic. Current testbeds, however, are unable to automate the generation and labelling of realistic network traffic. To address this, we describe a testbed which automates applications on mobile devices to generate and label realistic traffic. From this testbed, two labelled datasets of network traffic have been created. We provide an analysis of the testbed automation reliability and benchmark the datasets for the task of application classification.