| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | August 15, 2016 |
| Latest Amendment Date: | August 15, 2016 |
| Award Number: | 1649372 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Alexander Sprintson
CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | October 1, 2016 |
| End Date: | September 30, 2019 (Estimated) |
| Total Intended Award Amount: | $299,730.00 |
| Total Awarded Amount to Date: | $299,730.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
160 ALDRICH HALL IRVINE CA US 92697-0001 (949)824-7295 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
CalIT2 Bldg Irvine CA US 92697-0001 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Networking Technology and Syst |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Mobile devices generate an ever-increasing volume of traffic, are used for a range of applications from communication to financial transactions, and have access to personal information. Since mobile user behavior as well as third-party activities eventually manifest themselves through using the network, passive network monitoring offers a unique opportunity to detect both legitimate and malicious activity patterns on the mobile device. This project proposes AntMonitor - a new framework for real-time, on-device, passive network monitoring and crowd-sourcing. The goal is to understand, learn and control patterns in network activity, for applications related to privacy, security, performance, and behavioral analysis. The research agenda promotes transparency of mobile data, puts the user in control of how her data are shared or monetized, and can inform policy makers. Given today's size and personal nature of mobile data, changing the practices of how mobile devices handle and share our information can have significant societal impact, primarily in terms of privacy and security and secondarily in terms of the economics of personal data. In addition, the project will train students and minorities, and will provide software tools and data sets to the research community.
This project will build and deploy AntMonitor - a system for collection and analysis of fine-grained, large-scale, passive network measurements from mobile devices. Design challenges that will be addressed include high performance in terms of network throughput and battery consumption, and modular design so as to support different applications including (i) privacy leaks detection and prevention (ii) learning of user and app behavior and anomaly detection and (iii) network performance monitoring. Each of these application domains requires its own module in the AntMonitor framework and faces its own challenges in terms of system design, algorithms and data analysis. Overall, the project will advance the state-of-the-art in mobile network monitoring and will improve our understanding of patterns in mobile network activity. It will produce novel algorithms and data analysis methods that enhance the performance, security and privacy of mobile devices. A unique challenge lies in crowd-sourcing and deploying AntMonitor with real users in the wild. To this end, the project will explore different ways to popularize the technology, including user-facing apps, libraries, open-source software, and data-sets available to the community.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
INTELLECTUAL MERIT. Mobile devices carry sensitive information that is routinely transmitted over the network and is often collected for analytics and targeted advertising purposes. Users are typically unaware of this data collection and have little control over their information. In this project, we develop mobile software and machine learning techniques that provide users with more transparency and control over their mobile data.
We follow a network traffic-based approach: we develop AntMonitor: a user space app and SDK that can inspect, analyze and block, in real-time, all network traffic coming in and out of the mobile device. We then develop three distinct applications on top of AntMonitor. First, we develop AntShield, to detect and block outgoing traffic that contains personally identifiable information. Second, we develop NoMoAds: the first mobile-specific, cross-app, machine learning-based ad-blocker, and we show that it outperforms state-of-the-art filter lists. Third, we build AutoLabel: a system for automatically labeling which packets are generated by third-party advertisement or analytics libraries. This eliminates the need for manual labeling, which was the major bottleneck in creating filter lists and classifiers, and enables the detection of trackers (NoMoATS in PETS 2018) in addition to ads (NoMoAds in PETS 2020).
BROADER IMPACT: All the software and datasets from this project are made publicly available at: https://athinagroup.eng.uci.edu/projects/antmonitor/. The project trained several graduate students, and led to interactions with non-profits and developer communities. The NoMoAds paper was the recipient of the Andreas Pfitzmann Best Student Paper Award 2018.
Last Modified: 05/05/2020
Modified by: Athina Markopoulou
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