  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.

Networking and Internet Architecture;Cryptography and Security;Machine Learning