EAGER: A New Framework for Mobile Network Monitoring, Learning and Control

EAGER：移动网络监控、学习和控制的新框架

基本信息

批准号：
1649372
负责人：
Athina Markopoulou
金额：
$ 29.97万
依托单位：
University of California-Irvine
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-10-01 至 2019-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1649372&HistoricalAwards=false
关键词：
EAGER New Framework Mobile Network

项目摘要

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.

移动设备产生的流量不断增加，用于从通信到金融交易的一系列应用，并且可以访问个人信息。由于移动用户行为以及第三方活动最终会通过使用网络表现出来，因此被动网络监控提供了一个独特的机会来检测移动设备上的合法和恶意活动模式。该项目提出了 AntMonitor——一种用于实时、设备上、被动网络监控和众包的新框架。目标是了解、学习和控制网络活动模式，适用于与隐私、安全、性能和行为分析相关的应用程序。该研究议程提高了移动数据的透明度，让用户控制其数据的共享或货币化方式，并为政策制定者提供信息。鉴于当今移动数据的规模和个人性质，改变移动设备处理和共享信息的做法可能会产生重大的社会影响，主要是在隐私和安全方面，其次是在个人数据的经济方面。此外，该项目还将培训学生和少数族裔，并向研究界提供软件工具和数据集。该项目将构建和部署 AntMonitor——一个用于收集和分析来自移动设备的细粒度、大规模、无源网络测量结果的系统。将解决的设计挑战包括网络吞吐量和电池消耗方面的高性能以及模块化设计，以支持不同的应用程序，包括（i）隐私泄漏检测和预防（ii）学习用户和应用程序行为以及异常检测和（ iii) 网络性能监控。每个应用领域都需要在AntMonitor框架中拥有自己的模块，并在系统设计、算法和数据分析方面面临着自己的挑战。总体而言，该项目将推进移动网络监控的最先进水平，并将提高我们对移动网络活动模式的理解。它将产生新颖的算法和数据分析方法，以增强移动设备的性能、安全性和隐私性。一个独特的挑战在于与真实用户进行众包和部署 AntMonitor。为此，该项目将探索推广该技术的不同方式，包括面向用户的应用程序、库、开源软件和社区可用的数据集。