@INPROCEEDINGS{aceto2019mirage,
author={G. {Aceto} and D. {Ciuonzo} and A. {Montieri} and V. {Persico} and A. {Pescap{\`e}}},
booktitle={IEEE 4th International Conference on Computing, Communication and Security (ICCCS 2019)},
title={MIRAGE: Mobile-app Traffic Capture and Ground-truth Creation},
year={2019},
volume={},
number={},
pages={},
abstract={Network traffic analysis, i.e. the umbrella of procedures for distilling information from network traffic, represents the enabler for highly-valuable profiling information, other than being the workhorse for several key network management tasks. While it is currently being revolutionized in its nature by the rising share of traffic generated by mobile and hand-held devices, existing design solutions are mainly evaluated on private traffic traces, and only a few public datasets are available, thus clearly limiting repeatability and further advances on the topic. To this end, this paper introduces and describes MIRAGE, a reproducible architecture for mobile-app traffic capture and ground-truth creation. The outcome of this system is MIRAGE-2019, a human-generated dataset for mobile traffic analysis (with associated ground-truth) having the goal of advancing the state-of-the-art in mobile app traffic analysis. A first statistical characterization of the mobile-app traffic in the dataset is provided in this paper. Still, MIRAGE is expected to be capitalized by the networking community for different tasks related to mobile traffic analysis.},
keywords={Android apps; encrypted traffic; mobile apps; mobile traffic; reproducible research; open dataset; traffic classification},
doi={},
ISSN={},
month={Oct},}