Abstract
<p>A random number can be defined as a set of numbers produced by a numerical function, in which the next number is unpredictable and a relationship between successive occurrences is lacking. Moreover, these sequences cannot be reproduced unless the same generator function with an exact initial value is used. The design of a random number generator must overcome the previous problems of a low periodic and the capacity to reproduce the same sequence. This paper proposes the knight tour as a tool for generating pseudo random numbers. These random numbers can be use in the encryption process or in a password generator for network administrators. The randomness test suite is used to ensure the randomness of outcome sequences. Roughly, 75% of the test results obtained is better than the results from other works. The statistical properties and security analysis indicate that the knight tour application is highly successful in generating a pseudo random number with good statistical results, high linear complexity and strong capacity to withstand attacks.</p>
Highlights
Random numbers are widely used for many applications, such as keys for encryption and decryption, numerical analysis, simulating and modelling, as well as for selecting random samples from larger data sets (Li, 2012; Mahmood & Rahim, 2014; Tong, Liu, Zhang, Xu, & Wang, 2015)
Some of the previous generators fail in randomness statistical tests because the generated sequences are insufficiently random (Pashley, 2014). To overcome these issues and obtain a good random sequence, this paper proposes the use of the knight tour problem, which is described in detail
The process of generating random numbers begins with the specification of the start cell of the knight in an 8 × 8 chessboard
Summary
Random numbers are widely used for many applications, such as keys for encryption and decryption, numerical analysis, simulating and modelling, as well as for selecting random samples from larger data sets (Li, 2012; Mahmood & Rahim, 2014; Tong, Liu, Zhang, Xu, & Wang, 2015). A random number can be generated by measuring random physical phenomena, such as temperature, wind speed and sunlight level This type of generator is called a true random number generator (TRNG). TRNG requires additional equipment to produce random numbers and lacks the capacity to regenerate the same sequence unless the same initial key is used. It hardly ever regenerates the same random sequence because the sequence produced comes from the natural particular physical phenomenon. Another type of generator, called pseudorandom number generator (PRNG), uses mathematical algorithms to generate the random numbers. PRNGs are periodic generators, that is, they regenerate the same sequence after a certain round
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