Abstract
This paper presents a criterion, based on information theory, to measure the amount of average information provided by the sequences of outputs of the RC4 on the internal state. The test statistic used is the sum of the maximum plausible estimates of the entropies , corresponding to the probability distributions of the sequences of random variables and , independent, but not identically distributed, where are the known values of the outputs, while is one of the unknown elements of the internal state of the RC4. It is experimentally demonstrated that the test statistic allows for determining the most vulnerable RC4 outputs, and it is proposed to be used as a vulnerability metric for each RC4 output sequence concerning the iterative probabilistic attack.
Highlights
In [1], the iterative probabilistic attack was proposed to reconstruct the internal state of the RC4 algorithm, starting from knowing an output sequence, which was successively improved in [2,3]
The test statistic used is based on the entropy of the conditional probability distributions P( jt |zt ) for the zt that appear in the evaluated sample
The lower the value of the statistic, the more vulnerable the evaluated sample is the lower the attacker’s uncertainty will be about the value of the variable jt. This result can have various applications, since it allows for an evaluation of a set of RC4 output sequences according to their vulnerability or theoretical strength in face of iterative probabilistic attacks
Summary
In [1], the iterative probabilistic attack was proposed to reconstruct the internal state of the RC4 algorithm, starting from knowing an output sequence, which was successively improved in [2,3]. The lower the value of the statistic, the more vulnerable the evaluated sample is the lower the attacker’s uncertainty will be about the value of the variable jt This result can have various applications, since it allows for an evaluation of a set of RC4 output sequences according to their vulnerability or theoretical strength in face of iterative probabilistic attacks. This criterion can characterize the keys that cause the greatest vulnerability, which can lead to the identification of a new class of weak keys. The results of applying the statistic on RC4 output sequences are illustrated; Section 6 presents some conclusions
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
