SaTC: CORE: Small: Collaborative: A Multi-Layer Learning Approach to Mobile Traffic Filtering

SaTC：核心：小型：协作：移动流量过滤的多层学习方法

• 批准号: 1815131
• 负责人: Zubair Shafiq
• 金额: $ 25万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815131&HistoricalAwards=false
• 关键词: SaTC; CORE; Small; Collaborative; Multi

The mobile ecosystem has become an attractive target for various types of abuses. For instance, many mobile applications leak sensitive user information, such as email addresses and location, which is a privacy issue. Second, attackers routinely disguise malware in seemingly legitimate mobile apps to launch attacks, which poses security threats. Third, many mobile apps and sites push intrusive und undesirable ads, such as auto-play and pop-ups, which harm usability. The objective of this project is to defend against these abuses in the mobile ecosystem through real-time on-device filtering of network traffic on a mobile device.The project performs multi-layer network traffic analysis with help of machine learning. It extracts features and train learning models to produce filtering rules, which can be applied on the device. Filtering on the mobile device is particularly challenging due to limited resources, lack of visibility into packet payload, and potential collateral damage on the affected apps. The filtering approach used is envisioned as universal (i.e., applicable across all apps), automated (compared to the current practice of manually maintained privacy related data), adaptive (to the ever-changing threats), and user-centric (i.e., customized per user) while leveraging collaboration among users. The general framework will be validate in three case studies against the following abuse scenarios in the mobile ecosystem: (i) intrusive ads (ii) privacy leaks (iii) malvertising. This project is excepted to enhance the usability, privacy, and security of the mobile ecosystem and will inform policies and practices on mobile data transparency.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

移动的生态系统已成为各种滥用行为的诱人目标。例如，许多移动的应用程序泄漏敏感的用户信息，例如电子邮件地址和位置，这是隐私问题。其次，攻击者通常会在看似合法的移动的应用程序中伪装恶意软件来发动攻击，这会带来安全威胁。第三，许多移动的应用程序和网站推送侵入性和不受欢迎的广告，如自动播放和弹出窗口，这损害了可用性。该项目的目标是通过对移动终端上的网络流量进行实时的设备上过滤来防御移动的生态系统中的这些滥用行为。该项目在机器学习的帮助下执行多层网络流量分析。它提取特征并训练学习模型以产生过滤规则，这些规则可以应用于设备上。在移动终端上进行过滤尤其具有挑战性，因为资源有限，无法查看数据包有效负载，而且可能会对受影响的应用造成附带损害。所使用的滤波方法被设想为通用的（即，适用于所有应用程序）、自动化（与手动维护隐私相关数据的当前实践相比）、自适应（对于不断变化的威胁），以及以用户为中心（即，每个用户定制），同时利用用户之间的协作。该总体框架将在三个案例研究中针对移动的生态系统中的以下滥用场景进行验证：（i）侵入性广告（ii）隐私泄露（iii）恶意广告。该项目旨在提高移动的生态系统的可用性、隐私性和安全性，并将为移动的数据透明度的政策和实践提供信息。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Inferring Tracker-Advertiser Relationships in the Online Advertising Ecosystem using Header Bidding

• DOI: 10.2478/popets-2020-0001
• 发表时间: 2019-07
• 期刊: Proceedings on Privacy Enhancing Technologies
• 作者: John Cook;Rishab Nithyanand;Zubair Shafiq

ShadowBlock: A Lightweight and Stealthy Adblocking Browser

• DOI: 10.1145/3308558.3313558
• 发表时间: 2019-05
• 期刊: The World Wide Web Conference
• 作者: Shitong Zhu;Umar Iqbal;Zhongjie Wang;Zhiyun Qian;Zubair Shafiq;Weiteng Chen

AdGraph: A Graph-Based Approach to Ad and Tracker Blocking

• DOI: 10.1109/sp40000.2020.00005
• 发表时间: 2018-05
• 期刊: 2020 IEEE Symposium on Security and Privacy (SP)
• 作者: Umar Iqbal;Peter Snyder;Shitong Zhu;B. Livshits;Zhiyun Qian;Zubair Shafiq