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New results on generalization of roos-type biases and related keystreams of RC4
Maitra S., Paul G., , Lehmann M., Meier W.
Published in Springer Verlag
2013
Volume: 7918 LNCS
   
Pages: 222 - 239
Abstract
The first known result on RC4 cryptanalysis (presented by Roos in 1995) points out that the most likely value of the y-th element of the permutation after the key scheduling algorithm (KSA) for the first few values of y is given by S N [y] = f y, some linear combinations of the secret keys. While it should have been quite natural to study the association S N [y] = f y ±t for small positive integers t (e.g., t ≤ 4), surprisingly that had never been tried before. In this paper, we study that problem for the first time and show that though the event S N [y] = f y + t occurs with random association, there is a significantly high probability for the event S N [y] = f y - t. We also present several related non-randomness behaviour for the event S N [S N [y]] = f y - t of RC4 KSA in this direction. Further, we investigate near-colliding keys that lead to related states after the KSA and related keystream bytes. Our investigation reveals that near-colliding states do not necessarily lead to near-colliding keystreams. From this motivation, we present a heuristic to find a related key pair with differences in two bytes, that lead to significant matches in the initial keystream. In the process, we discover a class of related key distinguishers for RC4. The best one of these shows that given a random key and a related one to that (the last two bytes increased and decreased by 1 respectively), the first pair of bytes corresponding to the related keys are same with very high probability (e.g., approximately 0.011 for 16-byte keys to 0.044 for 30-byte keys). © 2013 Springer-Verlag Berlin Heidelberg.
About the journal
JournalData powered by TypesetLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherData powered by TypesetSpringer Verlag
ISSN03029743
Open AccessNo