MIRAGE: Mobile-app Traffic Capture and Ground-truth Creation

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.

[1]  Antonio Pescapè,et al.  Multi-classification approaches for classifying mobile app traffic , 2018, J. Netw. Comput. Appl..

[2]  Wouter Joosen,et al.  Automated Feature Extraction for Website Fingerprinting through Deep Learning. , 2017 .

[3]  Ali A. Ghorbani,et al.  Characterization of Tor Traffic using Time based Features , 2017, ICISSP.

[4]  Qusay H. Mahmoud,et al.  Dataset for Web Traffic Security Analysis , 2018, IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society.

[5]  Vijay Sivaraman,et al.  Classifying IoT Devices in Smart Environments Using Network Traffic Characteristics , 2019, IEEE Transactions on Mobile Computing.

[6]  Antonio Pescapè,et al.  Issues and future directions in traffic classification , 2012, IEEE Network.

[7]  Klaus Wehrle,et al.  The Dagstuhl beginners guide to reproducibility for experimental networking research , 2019, CCRV.

[8]  Zhen Liu,et al.  Benchmark Data for Mobile App Traffic Research , 2018, MobiQuitous.

[9]  Antonio Pescapè,et al.  Identification of Traffic Flows Hiding behind TCP Port 80 , 2010, 2010 IEEE International Conference on Communications.

[10]  Michael Seufert,et al.  A Public Dataset for YouTube's Mobile Streaming Client , 2018, 2018 Network Traffic Measurement and Analysis Conference (TMA).

[11]  Antonio Pescapè,et al.  Internet traffic modeling by means of Hidden Markov Models , 2008, Comput. Networks.

[12]  Andra Lutu,et al.  Open collaborative hyperpapers: a call to action , 2019, CCRV.

[13]  Packet Momentum for Identification of Anonymity Networks , 2017 .

[14]  Giuseppe Aceto,et al.  Mobile Encrypted Traffic Classification Using Deep Learning: Experimental Evaluation, Lessons Learned, and Challenges , 2019, IEEE Transactions on Network and Service Management.

[15]  Sami Souihi,et al.  A Novel QUIC Traffic Classifier Based on Convolutional Neural Networks , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[16]  Ali A. Ghorbani,et al.  Characterization of Encrypted and VPN Traffic using Time-related Features , 2016, ICISSP.

[17]  Mauro Conti,et al.  Robust Smartphone App Identification via Encrypted Network Traffic Analysis , 2017, IEEE Transactions on Information Forensics and Security.

[18]  Ran Dubin,et al.  I Know What You Saw Last Minute—Encrypted HTTP Adaptive Video Streaming Title Classification , 2016, IEEE Transactions on Information Forensics and Security.

[19]  Antonio Pescapè,et al.  K-Dimensional Trees for Continuous Traffic Classification , 2010, TMA.