Abstract
In recent years, a chaotic system is considered as an important pseudo-random source to pseudo-random number generators (PRNGs). This paper proposes a PRNG based on a modified logistic chaotic system. This chaotic system with fixed system parameters is convergent and its chaotic behavior is analyzed and proved. In order to improve the complexity and randomness of modified PRNGs, the chaotic system parameter denoted by floating point numbers generated by the chaotic system is confused and rearranged to increase its key space and reduce the possibility of an exhaustive attack. It is hard to speculate on the pseudo-random number by chaotic behavior because there is no statistical characteristics and infer the pseudo-random number generated by chaotic behavior. The system parameters of the next chaotic system are related to the chaotic values generated by the previous ones, which makes the PRNG generate enough results. By confusing and rearranging the output sequence, the system parameters of the previous time cannot be gotten from the next time which ensures the security. The analysis shows that the pseudo-random sequence generated by this method has perfect randomness, cryptographic properties and can pass the statistical tests.
Highlights
With the rapid development of communication technology and the wide use of the Internet and mobile networks, people pay more and more attention to information security
pseudo-random number generators (PRNGs), the chaotic system parameter denoted by floating point numbers generated by the chaotic system is confused and rearranged to increase its key space and reduce the possibility of an exhaustive attack
This paper proposes a PRNG based on a logistic chaotic system
Summary
With the rapid development of communication technology and the wide use of the Internet and mobile networks, people pay more and more attention to information security. In 2013, François M et al presented a PRNG algorithm based on mixing three chaotic maps produced from an input initial vector [13] which uses the standard chaos function and linear congruence to calculate and index the arranged position and passes the test of the NIST test suite. Many researchers have proposed similar feedback approaches [9,27,28] such as Patidar achieved the continuous operation of PRNGs by using the value generated by the previous chaotic system as the initial value of the iteration of chaotic systems [9]. The research of PRNG based on chaos focuses on the complexity of random bit extraction which attaches importance to reduce the possibility of extracting chaotic information by improving the complexity of the algorithm. It was found that it had good randomness and no obvious statistical information
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