A Testbed for Automating and Analysing Mobile Devices and their Applications

TL;DR

This paper introduces a novel automated testbed that generates and labels realistic mobile device network traffic, facilitating machine learning research for improved network situational awareness and device classification.

Contribution

The paper presents a new automated testbed for generating and labeling mobile device network traffic, along with two benchmark datasets for application classification.

Findings

Testbed automation is reliable for generating labeled traffic.

Benchmark datasets enable application classification research.

Automated traffic generation improves network security analysis.

Abstract

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.