Not so greedy : Enhanced subset exploration for nonrandomness detectors
(2018) International Conference on Information Systems Security and Privacy In Communications in Computer and Information Science 867. p.273-294- Abstract
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... (More)
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.
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- author
- Karlsson, Linus LU ; Hell, Martin LU and Stankovski, Paul LU
- organization
- publishing date
- 2018-01-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Distinguisher, Grain-128, Grain-128a, Kreyvium, Maximum degree monomial, Nonrandomness detector
- host publication
- Information Systems Security and Privacy - 3rd International Conference, ICISSP 2017, Revised Selected Papers
- series title
- Communications in Computer and Information Science
- volume
- 867
- pages
- 22 pages
- publisher
- Springer
- conference name
- International Conference on Information Systems Security and Privacy
- conference location
- Porto, Portugal
- conference dates
- 2017-02-19 - 2017-02-21
- external identifiers
-
- scopus:85049107964
- ISSN
- 1865-0929
- ISBN
- 9783319933535
- DOI
- 10.1007/978-3-319-93354-2_13
- language
- English
- LU publication?
- yes
- id
- 971eb793-8fba-446e-96e1-a69cbe6d1cfe
- date added to LUP
- 2018-07-09 13:37:40
- date last changed
- 2023-03-30 14:53:30
@inproceedings{971eb793-8fba-446e-96e1-a69cbe6d1cfe, abstract = {{<p>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.</p>}}, author = {{Karlsson, Linus and Hell, Martin and Stankovski, Paul}}, booktitle = {{Information Systems Security and Privacy - 3rd International Conference, ICISSP 2017, Revised Selected Papers}}, isbn = {{9783319933535}}, issn = {{1865-0929}}, keywords = {{Distinguisher; Grain-128; Grain-128a; Kreyvium; Maximum degree monomial; Nonrandomness detector}}, language = {{eng}}, month = {{01}}, pages = {{273--294}}, publisher = {{Springer}}, series = {{Communications in Computer and Information Science}}, title = {{Not so greedy : Enhanced subset exploration for nonrandomness detectors}}, url = {{https://lup.lub.lu.se/search/files/47782333/karlsson_notsogreedy.pdf}}, doi = {{10.1007/978-3-319-93354-2_13}}, volume = {{867}}, year = {{2018}}, }