Robust Covert Wireless Communications: Fundamental Limits and Algorithms

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Wireless communication networks form a critical part of the national infrastructure for both civilian and military applications. Because these networks propagate a radio signal that can be readily observed, security and privacy are major concerns. Much of security and privacy research focuses on protecting the content of messages from eavesdroppers, but there are important applications where even the detection of a communication signal's presence by an adversary is undesirable. In a civilian setting, the detection of a signal from an embedded medical device leaks health information, or the detection of encrypted signals transmitted between parties can lead to that communication being shut down by an authoritarian government. In a military setting, the presence of communication signals can be used as a proxy for troop activity. Hence, undetectable or low probability of detection (LPD) communications has motivated significant historical and recent study. Protection against the detection of a radio signal is challenging given recent advances in algorithms and computation. Algorithmic advances through techniques based on artificial intelligence (AI) and computational advances based on quantum computing provide the adversary with a powerful set of tools for developing a signal detector. This has motivated a branch of information systems research initiated by a portion of this research team ten years ago, now termed “covert communications,” that is developing a fundamental understanding of the ability of two parties to communicate without detection by an attentive and capable adversary. This project will develop results in covert communications for the models that underpin radio communications to allow for the protection of messages in the wireless communication systems that are crucial to modern society.For discrete-time additive white Gaussian noise (AWGN) channels, Bash, Goeckel, and Towsley initiated recent covert communications work by demonstrating in 2012 that a transmitter Alice can reliably transmit O(sqrt(n)) bits in n channel uses (and no more) to recipient Bob without detection by an attentive and capable adversary Willie. And nearly all work in covert communications has been performed on discrete-time models, with the implicit understanding that the results would be applicable to the true continuous-time system. But this is not true for important emerging architectures for covert wireless communications; rather, the continuous-time channel must be considered directly. This project will consider the fundamental characterization of covert communication systems as they are considered for implementation in wireless communications through three research thrusts: 1. Foundations of covert communications in continuous-time systems: We focus on performance characterization and design in the face of tools available for the adversary Willie in continuous-time wireless systems: interference cancellation, cyclostationary detectors, and transmitter identification. 2. Covert communications in the presence of environmental interference: Covert communications hidden behind environmental signals is preferable to employing jamming. We focus on covert throughput bounds as a function of the interference dynamics and techniques to achieve those bounds. 3. Learning-based covert communication approaches: We design and analyze deep neural network (DNN)-based covert transceivers by the simultaneous training of an adversary whose performance is used as a regularizer in the training of the transmitter Alice. The training of a diverse workforce in security and privacy is an important aspect of the project. The third thrust that considers AI-based approaches at both the communication parties and the adversary provides an accessible and highly desirable research area for University of Massachusetts undergraduate students in engineering and computer science.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.