Publication Date
Summer 2021
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Mark Stamp
Second Advisor
Fabio Di Troia
Third Advisor
Mike Wu
Keywords
GAN, CipherGAN, HMM
Abstract
The necessity of protecting critical information has been understood for millennia. Although classic ciphers have inherent weaknesses in comparison to modern ciphers, many classic ciphers are extremely challenging to break in practice. Machine learning techniques, such as hidden Markov models (HMM), have recently been applied with success to various classic cryptanalysis problems. In this research, we consider the effectiveness of the deep learning technique CipherGAN---which is based on the well- established generative adversarial network (GAN) architecture---for classic cipher cryptanalysis. We experiment extensively with CipherGAN on a number of classic ciphers, and we compare our results to those obtained using HMMs.
Recommended Citation
Charan, Deanne, "Generative Adversarial Networks for Classic Cryptanalysis" (2021). Master's Projects. 1034.
DOI: https://doi.org/10.31979/etd.h2mh-uh52
https://scholarworks.sjsu.edu/etd_projects/1034
