On Guess and Determine Cryptanalysis of LFSR-Based Stream Ciphers
In this paper, the complexity of applying a guess and determine attack to so-called Linear Feedback Shift register (LFSR)-based stream ciphers is analyzed. This family of stream ciphers uses a single or several LFSR and a filtering function F : GF(2) n rarr GF(2) m to generate the blocks of m ges 1...
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| Vydané v: | IEEE transactions on information theory Ročník 55; číslo 7; s. 3398 - 3406 |
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| Hlavný autor: | |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York, NY
IEEE
01.07.2009
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Predmet: | |
| ISSN: | 0018-9448, 1557-9654 |
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| Shrnutí: | In this paper, the complexity of applying a guess and determine attack to so-called Linear Feedback Shift register (LFSR)-based stream ciphers is analyzed. This family of stream ciphers uses a single or several LFSR and a filtering function F : GF(2) n rarr GF(2) m to generate the blocks of m ges 1 keystream bits at the time. In difference to a classical guess and determine attack, a method based on guessing certain bits in order to determine the remaining secret key/state bits, our approach efficiently takes advantage of the reduced preimage space for relatively large m and at the same time employing the design structure of the cipher. Several variations of the algorithm are derived to circumvent the sensitivity of attack to the input data, n, m and the key length. In certain cases, our attack outperforms classical algebraic attacks; these being considered as one of the most efficient cryptanalyst tools for this type of ciphers. A superior performance of our attack over algebraic attacks is demonstrated in case the filtering function belongs to the extended Maiorana-McFarland class. |
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| Bibliografia: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 |
| ISSN: | 0018-9448 1557-9654 |
| DOI: | 10.1109/TIT.2009.2021316 |