# A Testbed for Automating and Analysing Mobile Devices and their Applications

arXiv: https://arxiv.org/abs/2309.08158 · HTML: https://mlsanity.com/paper/2309.08158/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 15 Sep 2023.

## Builds on (most cited first, 3 of 3)

- [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://mlsanity.com/paper/2012.05345/) (2020)
- [Robust Smartphone App Identification Via Encrypted Network Traffic Analysis](https://mlsanity.com/paper/1704.06099/) (2017)
- [The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis](https://mlsanity.com/paper/1708.03766/) (2017)

## Built on by (most cited first, 1 of 1)

- [Algebraic Adversarial Attacks on Explainability Models](https://mlsanity.com/paper/2503.12683/) (2025)

## Abstract (arXiv)

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.