SaTC: CORE: Small: Side-channel Attacks Against Mobile Users: Singularity Detection, Behavior Identification, and Automated Rectification

侧信道攻击已被证明可以有效地推断不应向未授权用户泄露的敏感信息（如用户活动）。由于蜂窝网络基础设施的封闭性，攻击者不能轻易捕获加密的移动网络流量，从而防止移动用户的侧信道信息泄露。然而，随着最近软件定义无线电平台和新兴的基于互联网协议的蜂窝网络服务在公共网络（包括Wi-Fi呼叫）上的扩散，移动电话用户现在面临着比以前更严重的侧信道信息泄露。本项目旨在通过收集、标记、挖掘和分析移动用户的加密移动数据，对手机用户的侧信道攻击进行全面调查。这项研究的成功不仅将为发现可以从侧信道信息泄漏中利用的安全漏洞提供新技术，而且还将开发新的自动纠正机制来保护用户。拟议的活动可能有助于即将到来的5G技术标准化，并培养新一代工程师和学生。本项目有三个技术贡献：(1)移动数据收集和标记的新技术：蜂窝网络控制平面信号表明各种蜂窝网络事件（如用户服务质量配置文件或位置的变化），这些事件可能被利用来侵犯用户隐私。然而，当前一代的蜂窝嗅探器不能很好地区分控制平面信号和数据平面数据包，当它们在同一物理信道上传输时。该项目将开发收集加密移动网络流量的新技术，包括控制平面信号和数据平面数据包，并将其标记为用户行为和网络事件；(2)先进的奇点检测和行为识别机制：本项目将研究开发能够同时进行奇点检测和行为识别的端到端框架。这包括处理有限的标记数据和挖掘新出现的行为的频繁模式；(3)移动/蜂窝友好的自动纠偏机制：针对侧信道攻击的最先进的安全防御并不是为移动网络系统设计的。例如，移动用户要为添加到数据包中的噪声支付性能损失。该项目将开发移动友好型（意味着低内存使用量）和蜂窝友好型（意味着与标准和运营商的收费模式兼容）自动纠错机制，以确保各种移动设备的安全。该奖项反映了美国国家科学基金会的法定使命，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。

Side-channel attacks have been proven effective to infer sensitive information (such as user activities) that should not be disclosed to unauthorized users. Owing to the closed nature of the cellular network infrastructure, adversaries cannot easily capture encrypted mobile network traffic, thus protecting against side-channel information leakage of mobile users. However, with the recent proliferation of software defined radio platforms and emerging Internet Protocol-based cellular network services over public networks (including Wi-Fi calling), mobile phone users are now exposed to more serious side-channel information leakage than before. This project aims to conduct a comprehensive investigation of side-channel attacks against mobile phone users by collecting, labeling, mining, and analyzing mobile users' encrypted mobile data. The success of this research will not only contribute new techniques to discover security vulnerabilities that can be exploited from side-channel information leakage, but also develop novel automated rectification mechanisms to safeguard users. The proposed activities may contribute to the upcoming 5G technology standardization and train a new generation of engineers and students. This project makes three technical contributions: (1) New techniques for mobile data collection and labeling: Cellular network control-plane signals indicate a variety of cellular network events (such as changes in a user's Quality-of-Service profile or location) which may be exploited to invade user privacy. However, the current-generation cellular sniffers cannot distinguish well between control-plane signals and data-plane data packets when they are transmitted over the same physical channel. This project will develop new techniques to collect encrypted mobile network traffic including control-plane signals and data-plane data packets and label them with user behaviors and network events; (2) Advanced Singularity Detection and Behavior Identification mechanism: This project will study and develop end-to-end frameworks that can perform singularity detection and behavior identification simultaneously. This involves processing limited labeled data and mining frequent patterns for emerging behaviors; (3) Mobile/cellular-friendly automated rectification mechanisms: The state-of-the-art security defenses for side-channel attacks are not designed for mobile networked systems. For example, mobile users pay a performance penalty for the noise added to their data packets. This project will develop mobile-friendly (meaning low memory usage) and cellular-friendly (meaning compatible with standards and operators' charging model) automated rectification mechanisms to secure a variety of mobile devices.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.