Emma Meno

Emma Meno

Research Associate, Intelligent Systems Division

Emma Meno is a Research Associate with the Intelligent Systems Division at Virginia Tech National Security Institute. Emma’s technical expertise centers at the intersection of cybersecurity and machine learning. At VTNSI, she is the primary technical contributor for various cybersecurity projects and topics, including cryptographic authentication with physical unclonable functions (PUFs), reinforcement learning in attack domains, penetration testing for virtual twin security, and distributed ledger technology (DLT) in satellite applications. Emma previously worked at Kudu Dynamics LLC as a full-time security researcher on a “Voice of the Offense” purple team cybersecurity contract. As part of that role, she evaluated and researched the frequency and nature of performers’ PDF parser differentials (i.e. encoding anomaly variations). During the last months of her Kudu position, Emma developed a tool to perform isolated smashes on raw PDF files to trigger such parser differentials. Emma’s graduate thesis work focused on neural cryptanalysis, aiming to study cipher strength via a novel black-box machine learning approach. Her work utilized supervised learning to train neural networks to predict encrypted bits given a set of plaintext/ciphertext pairs. The harder it was to mimic the encryption (i.e. the lower the bitwise prediction accuracy), the more secure the cipher.

Emma graduated with her M.S. in Computer Science and Applications at Virginia Tech in May 2021. She was selected as one of five finalists to compete for a 2022 Paul E. Torgersen Graduate Student Research Award in the M.S. Poster Presentation category for her thesis work. Previously, she earned her B.S. in Computer Science, Minor in Mathematics, and Honors Laureate Diploma at Virginia Tech in May 2020 after three years of collegiate study as part of the Accelerated Master’s Degree Program. She earned the sole Outstanding Senior Award for the Department of Computer Science.