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

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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.