On the effectiveness of using state-of-the-art machine learning techniques to launch cryptographic distinguishing attacks

Jung Wei Chou*, Shou De Lin, Chen Mou Cheng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Scopus citations

Abstract

Cryptographic distinguishing attacks, in which the attacker is able to extract enough "information" from an encrypted message to distinguish it from a piece of random data, allow for powerful cryptanalysis both in theory and in practice. In this paper, we report our experience of applying state-of-the-art machine learning techniques to launch cryptographic distinguishing attacks on several public datasets. We try several kinds of existing and new features on these datasets and find that the ciphers' "modes of operation" dominate the performance of classification tasks. When CBC mode is used with a random initial vector for each plaintext, the performance is extremely bad, while the performance for certain datasets is relatively good when ECB mode is used. We conclude that, in contrary to the findings of several existing works, the state-of-the-art machine learning techniques cannot extract useful information from ciphertexts produced by modern ciphers operating in a reasonably secure mode such as CBC, let alone distinguish them from random data.

Original languageEnglish
Title of host publicationAISec'12 - Proceedings of the ACM Workshop on Security and Artificial Intelligence
Pages105-109
Number of pages5
DOIs
StatePublished - 2012
Externally publishedYes
Event5th ACM Workshop on Artificial Intelligence and Security, AISec 2012 - Raleigh, NC, United States
Duration: 19 10 201219 10 2012

Publication series

NameProceedings of the ACM Conference on Computer and Communications Security
ISSN (Print)1543-7221

Conference

Conference5th ACM Workshop on Artificial Intelligence and Security, AISec 2012
Country/TerritoryUnited States
CityRaleigh, NC
Period19/10/1219/10/12

Keywords

  • Computer Forensics
  • Cryptographic Distinguishing Attacks
  • Identification of Encryption Algorithm
  • Machine Learning

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