Date of Award

Spring 2020

Abstract

Monitoring mechanisms are a critical component of the security and maintenance of high precision timing networks. Any and all guarantees of determinism and correctness are invalidated if a synchronous network malfunctions or is compromised by an attacker. Existing mechanisms allow for a comprehensive view of the distribution of time throughout a network, but they do not scale to large networks. I propose a new method called aggregated reverse time transfer (ARTT), which redefines the existing mechanisms to include a new aggregation scheme that serves the dual purpose of distributed data summarization and anomaly detection for networks of any size. With this thesis I provide a full specification and implementation of the ARTT mechanism, test both the outlier detection and model accuracy on a real timing network, and detail the steps necessary to perform stable-state outlier detection and aggregation on large-scale networks.

First Advisor

Radim Bartos

Second Advisor

Robert Noseworthy

Third Advisor

Marek Petrik

Department or Program

Computer Science

Degree Name

Master of Science

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