Cited By
View all- Chandrasekhar VHeng YCho JLee JZhang JAndrews J(2019)Experience-Centric Mobile Video Scheduling Through Machine LearningIEEE Access10.1109/ACCESS.2019.29332737(113017-113030)Online publication date: 2019
Poster: Broadcast LTE Data Reveals Application Type
Authors: 
Arjun Balasingam, Manu Bansal, Rakesh Misra, Rahul Tandra, Aaron Schulman, Sachin KattiAuthors Info & Claims
Arjun Balasingam, Manu Bansal, Rakesh Misra, Rahul Tandra, Aaron Schulman, Sachin KattiAuthors Info & ClaimsPages 531 - 533
Abstract
The rapid growth in mobile connectivity is enabling phones to support a wide range of societally-important applications. In this work, we show that broad classes of popular mobile applications have distinct radio resource allocation signatures. Using this insight, we design a mobile application classifier, and demonstrate that (1) an application can infer its own type solely from its resource allocation patterns, and (2) anyone can accurately infer the type of application being served by each session on a particular cell tower. We present our findings by showing the breakdown of applications being served by an LTE base station belonging to a Tier 1 US provider in downtown Palo Alto. Our work encourages an open discussion about LTE standards, and whether they might need to be enhanced to mask features that can be exploited to infer application type from signals broadcast over the air.
References
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Cisco. Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2016--2021 White Paper. http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/mobile-white-paper-c11--520862.html, 2016},.
[2]
M. Kawser, H. Farid, A. Hasin, A. Sadik, and I. Razu. Performance comparison between round robin and proportional fair scheduling methods for lte. In International Journal of Information and Electronics Engineering, 2012.
[3]
S. Kumar, E. Hamed, D. Katabi, and L. E. Li. LTE Radio analytics made easy and accessible. In Proc. ACM SIGCOMM, 2014.
[4]
S. Lee, H.-c. Kim, D. Barman, S. Lee, C.-k. Kim, and T. T. Kwon. Netramark: A network traffic classification benchmark. In ACM SIGCOMM Computer Communication Review, 2011.
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QUALCOMM. Qualcomm extensible diagnostic monitor. https://www.qualcomm.com/documents/qxdm-professional-qualcomm-extensible-diagnostic-monitor.
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X. Xie, X. Zhang, S. Kumar, and L. E. Li. piStream: Physical layer informed adaptive video streaming over lte. In Proc. ACM Conference on Mobile Computing and Networking (MobiCom), 2015.
Index Terms
- Poster: Broadcast LTE Data Reveals Application Type
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Information & Contributors
Information
Published In
October 2017
628 pages
ISBN:9781450349161
DOI:10.1145/3117811
- General Chair:
- Kobus Van der Merwe,
- Program Chairs:
- Ben Greenstein,
- Kannan Srinivasan
Copyright © 2017 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 04 October 2017
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- Poster
Conference
MobiCom '17
Sponsor:
MobiCom '17: The 23rd Annual International Conference on Mobile Computing and Networking
October 16 - 20, 2017
Utah, Snowbird, USA
Acceptance Rates
MobiCom '17 Paper Acceptance Rate 35 of 186 submissions, 19%;
Overall Acceptance Rate 440 of 2,972 submissions, 15%
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Cited By
View all- Chandrasekhar VHeng YCho JLee JZhang JAndrews J(2019)Experience-Centric Mobile Video Scheduling Through Machine LearningIEEE Access10.1109/ACCESS.2019.29332737(113017-113030)Online publication date: 2019
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