Name
Abstract | ||
|---|---|---|
ABSTRACTCryptography and Machine Learning are two computational science fields that intuitively seem related. Privacy-preserving machine learning-either utilizing encrypted models or learning over encrypted data-is an exploding field thanks to the maturation of primitives such as fully homomorphic encryption and secure multiparty computation. However there has been surprisingly little work on applying recent advances in machine learning to the task of cryptanalysis, the branch of cryptography that studies how cryptographic ciphers can be attacked. In particular, while a cryptographic cipher seeks to keep certain information secret by making it appear random, discerning patterns and structure from random data is a common machine learning task. This paper proposes EveGAN, an approach that treats cryptanalysis as a language translation problem. While treating cipher cracking as a language translation problem has been validated against a handful of classical substitution ciphers, the EveGAN approach builds on these results to create a new class of generative deep learning-based cryptanalysis attacks. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3548606.3563493 | Computer and Communications Security |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roger Hallman | 1 | 14 | 7.73 |