Not so greedy: Enhanced subset exploration for nonrandomness detectors

Linus Karlsson, Martin Hell, Paul Stankovski

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingResearchpeer-review

1 Citation (SciVal)
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Distinguishers and nonrandomness detectors are used to distinguish ciphertext from random data. In this paper, we focus on the construction of such devices using the maximum degree monomial test. This requires the selection of certain subsets of key and IV-bits of the cipher, and since this selection to a great extent affects the final outcome, it is important to make a good selection. We present a new, generic and tunable algorithm to find such subsets. Our algorithm works on any stream cipher, and can easily be tuned to the desired computational complexity. We test our algorithm with both different input parameters and different ciphers, namely Grain-128a, Kreyvium and Grain-128. Compared to a previous greedy approach, our algorithm consistently provides better results.

Original languageEnglish
Title of host publicationInformation Systems Security and Privacy - 3rd International Conference, ICISSP 2017, Revised Selected Papers
Number of pages22
ISBN (Print)9783319933535
Publication statusPublished - 2018 Jan 1
EventInternational Conference on Information Systems Security and Privacy - Porto, Portugal
Duration: 2017 Feb 192017 Feb 21
Conference number: 3

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


ConferenceInternational Conference on Information Systems Security and Privacy
Abbreviated titleICISSP
Internet address

Subject classification (UKÄ)

  • Signal Processing


  • Distinguisher
  • Grain-128
  • Grain-128a
  • Kreyvium
  • Maximum degree monomial
  • Nonrandomness detector


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