SaTC: CORE: Medium: Collaborative: Defending against Compromise and Manipulation of Mobile Communities

SaTC：核心：媒介：协作：防御移动社区的妥协和操纵

• 批准号: 1834523
• 负责人: Ben Zhao
• 金额: $ 69.95万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1834523&HistoricalAwards=false
• 关键词: SaTC; CORE; Medium; Collaborative; Defending

Many of today's mobile services build mobile communities of users who share their valuable experiences and data. Examples include traffic incidents (Waze), restaurant reviews (Yelp, FourSquare), anonymous social networks (Whisper, Yik Yak), and even dating (Tinder, Bumble). Unfortunately, new threats can compromise and manipulate these communities, using lightweight software to mimic mobile devices. The resesarchers have shown how attackers can eavesdrop on mobile network traffic, learn their patterns, and write software to emulate mobile devices running the application. This amplifies existing attacks by multiple orders of magnitude, and allows attackers with limited resources to overwhelm mobile communities using millions of emulated devices under their control. These devices are difficult to detect, and completely cripple entire mobile systems, or manipulate them for the attacker's personal gain. Preliminary work showed that such an attack on Waze, Google's crowdsourced traffic navigation application, enabled fine grained GPS-level tracking of large user populations. Similar vulnerabilities apply to Yelp, Tinder, Uber, and others, with consequences ranging from manipulating/censoring content, theft of monetary incentives, to completely crippling the service.The work described in this proposal seeks a better understanding of the threat of software-emulated devices to mobile communities, and explores systematic defenses against them. The researchers will develop defenses that detect large-scale attacks and limit their impact to that of single misbehaving devices, using three orthogonal approaches: a) a centralized infrastructure-based solution, that uses hidden patterns in aggregate user data to authenticate mobile devices; b) hardware solutions that extracts device-specific sensor data using platform APIs, and compares them to known models of hardware data models; and c) application-level solutions that use unsupervised learning to automatically detect similarity clusters in devices, based on analysis of user behavior (clickstreams) and physical mobility trace.

当今的许多移动服务都建立了用户共享宝贵经验和数据的移动社区。例子包括交通事故（Waze）、餐厅评论（Yelp、FourSquare）、匿名社交网络（Whisper、Yik Yak），甚至约会（Tinder、Bumble）。 不幸的是，新的威胁可能会使用轻量级软件模仿移动设备，从而危害和操纵这些社区。 研究人员已经展示了攻击者如何窃听移动网络流量、了解其模式并编写软件来模拟运行该应用程序的移动设备。这将现有的攻击放大了多个数量级，并允许攻击者利用有限的资源利用其控制下的数百万个模拟设备来压垮移动社区。这些设备很难检测到，并且会完全削弱整个移动系统，或者为了攻击者的个人利益而操纵它们。 初步研究表明，对 Google 众包交通导航应用 Waze 的此类攻击可以对大量用户进行细粒度的 GPS 级跟踪。类似的漏洞也适用于 Yelp、Tinder、Uber 等，其后果包括操纵/审查内容、盗窃金钱激励、彻底瘫痪服务。本提案中描述的工作旨在更好地理解软件模拟的威胁设备到移动社区，并探索针对它们的系统防御。研究人员将使用三种正交方法开发防御措施，检测大规模攻击并将其影响限制在单个行为不当设备上：a）基于集中式基础设施的解决方案，使用聚合用户数据中的隐藏模式来验证移动设备； b) 使用平台 API 提取设备特定传感器数据的硬件解决方案，并将其与硬件数据模型的已知模型进行比较； c) 应用级解决方案，基于对用户行为（点击流）和物理移动轨迹的分析，使用无监督学习自动检测设备中的相似性集群。

项目成果

Gotta Catch'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks

消灭所有敌人：使用蜜罐捕获对神经网络的对抗性攻击

• DOI: 10.1145/3372297.3417231
• 发表时间: 2019-04-18
• 期刊: Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security
• 作者: Shawn Shan;Emily Wenger;Bolun Wang;B. Li;Haitao Zheng;Ben Y. Zhao
• 通讯作者: Ben Y. Zhao

Wearable Microphone Jamming

穿戴式麦克风干扰

• DOI: 10.1145/3313831.3376304
• 发表时间: 2020-04
• 期刊: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
• 作者: Chen, Yuxin;Li, Huiying;Teng, Shan;Nagels, Steven;Li, Zhijing;Lopes, Pedro;Zhao, Ben Y.;Zheng, Haitao
• 通讯作者: Zheng, Haitao

Automated Crowdturfing Attacks and Defenses in Online Review Systems

在线评论系统中的自动化 Crowdturfing 攻击和防御

• DOI: 10.1145/3133956.3133990
• 发表时间: 2017-08-27
• 期刊: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security
• 作者: Yuanshun Yao;Bimal Viswanath;Jenna Cryan;Haitao Zheng;Ben Y. Zhao
• 通讯作者: Ben Y. Zhao

Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks

神经清理：识别和减轻神经网络中的后门攻击

• DOI: 10.1109/sp.2019.00031
• 发表时间: 2019-04-01
• 期刊: 2019 IEEE Symposium on Security and Privacy (SP)
• 作者: Bolun Wang;Yuanshun Yao;Shawn Shan;Huiying Li;Bimal Viswanath;Haitao Zheng;Ben Y. Zhao
• 通讯作者: Ben Y. Zhao

Et Tu Alexa? When Commodity WiFi Devices Turn into Adversarial Motion Sensors

等你亚历克斯？

• DOI: 10.14722/ndss.2020.23053
• 发表时间: 2020-01
• 期刊: Network and Distributed Systems Security (NDSS
• 作者: Zhu, Yanzi;Xiao, Zhujun;Chen, Yuxin;Li, Zhijing;Liu, Ma;Zhao, Ben Y.;Zheng, Haitao
• 通讯作者: Zheng, Haitao