On Cryptographic Properties of LFSR-based Pseudorandom Generators

Pseudorandom Generators (PRGs) werden in der modernen Kryptographie verwendet, um einen kleinen Startwert in eine lange Folge scheinbar zufälliger Bits umzuwandeln. Viele Designs für PRGs basieren auf linear feedback shift registers (LFSRs), die so gewählt sind, dass sie optimale statistische und periodische Eigenschaften besitzen. Diese Arbeit diskutiert Konstruktionsprinzipien und kryptanalytische Angriffe gegen LFSR-basierte PRGs. Nachdem wir einen vollständigen Überblick über existierende kryptanalytische Ergebnisse gegeben haben, führen wir den dynamic linear consistency test (DLCT) ein und analysieren ihn. Der DLCT ist eine suchbaum-basierte Methode, die den inneren Zustand eines PRGs rekonstruiert. Wir beschließen die Arbeit mit der Diskussion der erforderlichen Zustandsgröße für PRGs, geben untere Schranken an und Beispiele aus der Praxis, die veranschaulichen, welche Größe sichere PRGs haben müssen

THE DYNAMIC CIPHERS – NEW CONCEPT OF LONG-TERM CONTENT PROTECTING

In the paper the original concept of a new cipher, targeted at this moment forcivil applications in technology (e.g. measurement and control systems) and business (e.g.content protecting, knowledge-based companies or long-term archiving systems) is presented.The idea of the cipher is based on one-time pads and linear feedback shift registers. Therapidly changing hardware and software environment of cryptographic systems has beentaken into account during the construction of the cipher. The main idea of this work is tocreate a cryptosystem that can protect content or data for a long time, even more than onehundred years. The proposed algorithm can also simulate a stream cipher which makes itpossible to apply it in digital signal processing systems such as those within audio and videodelivery or telecommunication.Content protection, Cryptosystem, Dynamic cryptography, Linear Feedback ShiftRegisters, Object-oriented programming, One-time pad, Random key, random number generators,Statistical evaluation of ciphers.

Comparison analysis of stream cipher algorithms for digital communication

The broadcast nature of radio communication such as in the HF (High Frequency) spectrum exposes the transmitted information to unauthorized third parties. Confidentiality is ensured by employing cipher system. For bulk transmission of data, stream ciphers are ideal choices over block ciphers due to faster implementation speed and not introducing error propagation. The stream cipher algorithms evaluated are based on the linear feedback shift register (LFSR) with nonlinear combining function. By using a common key length and worst case conditions, the strength of several stream cipher algorithms are evaluated using statistical tests, correlation attack, linear complexity profile and nonstandard test. The best algorithm is the one that exceeds all of the tests

Using classifiers to predict linear feedback shift registers

Proceeding of: IEEE 35th International Carnahan Conference on Security Technology. October 16-19, 2001, LondonPreviously (J.C. Hernandez et al., 2000), some new ideas that justify the use of artificial intelligence techniques in cryptanalysis are presented. The main objective of that paper was to show that the theoretical next bit prediction problem can be transformed into a classification problem, and this classification problem could be solved with the aid of some AI algorithms. In particular, they showed how a well-known classifier called c4.5 could predict the next bit generated by a linear feedback shift register (LFSR, a widely used model of pseudorandom number generator) very efficiently and, most importantly, without any previous knowledge over the model used. The authors look for other classifiers, apart from c4.5, that could be useful in the prediction of LFSRs. We conclude that the selection of c4.5 by Hernandez et al. was adequate, because it shows the best accuracy of all the classifiers tested. However, we have found other classifiers that produce interesting results, and we suggest that these algorithms must be taken into account in the future when trying to predict more complex LFSR-based models. Finally, we show some other properties that make the c4.5 algorithm the best choice for this particular cryptanalytic problem.Publicad