Good morning! You are cordially invited to Emma's MS thesis defense on neural cryptanalysis tomorrow. Her talk info is below. Have a nice day! *Thesis Title:* Neural Cryptanalysis for Cyber-Physical System Ciphers *Name:* Emma Meno *Date/Time: * April 30th, 2021 10:00 am *Zoom:* https://virginiatech.zoom.us/j/86495332689 Meeting ID: 864 9533 2689 *Abstract:* A key cryptographic research interest is developing an automatic, black-box method to provide a relative security strength measure for symmetric ciphers, particularly for proprietary cyber-physical systems (CPS) and lightweight block ciphers. This thesis work extends the work of the recently-developed neural cryptanalysis method, which trains neural networks on a set of plaintext/ciphertext pairs to extract meaningful bitwise relationships and predict corresponding ciphertexts given a set of plaintexts. As opposed to traditional cryptanalysis, the goal is not key recovery but achieving a mimic accuracy greater than a defined base match rate. In addition to reproducing tests run with the Data Encryption Standard, this work applies neural cryptanalysis to round-reduced versions and components of the SIMON/SPECK family of block ciphers and the Advanced Encryption Standard. This methodology generated a metric able to rank the relative strengths of rounds for each cipher as well as algorithmic components within these ciphers. Given the current neural network suite tested, neural cryptanalysis is best-suited for analyzing components of ciphers rather than full encryption models. If these models are improved, this method presents a promising future in measuring the strength of lightweight symmetric ciphers, particularly for CPS. *Bio:* Emma Meno is a MS student in the Virginia Tech Department of Computer Science. In June 2021, she will begin work as a research engineer at a cybersecurity defense contractor in Northern Virginia. Most of her work has been at the intersection of machine learning and cryptography. Emma has been recognized with a graduate research award from the department, along with multiple awards in her time as an undergraduate. She graduated with her B.S. in Computer Science at Virginia Tech in May 2020 as part of the Accelerated Master's program.