Tail-Specific Linear Approximations for Efficient Bootstrap Simulations

1995 ◽  
Vol 4 (2) ◽  
pp. 113 ◽  
Author(s):  
Tim Hesterberg
Author(s):  
Antonio Macaluso ◽  
Luca Clissa ◽  
Stefano Lodi ◽  
Claudio Sartori

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Wenqin Cao ◽  
Wentao Zhang

AbstractFor block ciphers, Bogdanov et al. found that there are some linear approximations satisfying that their biases are deterministically invariant under key difference. This property is called key difference invariant bias. Based on this property, Bogdanov et al. proposed a related-key statistical distinguisher and turned it into key-recovery attacks on LBlock and TWINE-128. In this paper, we propose a new related-key model by combining multidimensional linear cryptanalysis with key difference invariant bias. The main theoretical advantage is that our new model does not depend on statistical independence of linear approximations. We demonstrate our cryptanalysis technique by performing key recovery attacks on LBlock and TWINE-128. By using the relations of the involved round keys to reduce the number of guessed subkey bits. Moreover, the partial-compression technique is used to reduce the time complexity. We can recover the master key of LBlock up to 25 rounds with about 260.4 distinct known plaintexts, 278.85 time complexity and 261 bytes of memory requirements. Our attack can recover the master key of TWINE-128 up to 28 rounds with about 261.5 distinct known plaintexts, 2126.15 time complexity and 261 bytes of memory requirements. The results are the currently best ones on cryptanalysis of LBlock and TWINE-128.


Author(s):  
Joanofarc Xavier ◽  
S.K. Patnayak ◽  
Rames Panda

Abstract Several industrial chemical processes exhibit severe nonlinearity. This paper addresses the computational and nonlinear issues occurring in many typical industrial problems in aspects of its stability, strength of nonlinearity and input output dynamics. In this article, initially, a prospective investigation is conducted on various nonlinear processes through phase portrait analysis to understand their stability status at different initial conditions about the vicinity of the operating point of the process. To estimate the degree of nonlinearity, for input perturbations from its nominal value, a novel nonlinear measure is put forward, that anticipates on the converging area between the nonlinear and their linearized responses. The nonlinearity strength is fixed between 0 and 1 to classify processes to be mild, medium or highly nonlinear. The most suitable operating point, for which the system remains asymptotically stable is clearly identified from the phase portrait. The metric can be contemplated as a promising tool to measure the nonlinearity of Industrial case studies at different linear approximations. Numerical simulations are executed in Matlab to compute , which conveys that the nonlinear dynamics of each Industrial example is very sensitive to input perturbations at different linear approximations. In addition to the identified metric, nonlinear lemmas are framed to select appropriate control schemes for the processes based on its numerical value of nonlinearity..


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