This paper presents and discusses a method of generating encryption algorithms using neural networks and evolutionary computing. Based on the application of natural noise sources obtained from data that can include at...
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ISBN:
(纸本)9781479919598
This paper presents and discusses a method of generating encryption algorithms using neural networks and evolutionary computing. Based on the application of natural noise sources obtained from data that can include atmospheric noise (generated by radio emissions due to lightening, for example), radioactive decay, electronic noise and so on, we 'teach' a system to approximate the input noise with the aim of generating an output nonlinear function. This output is then treated as an iterator which is subjected to a range of tests to check for potential cryptographic strength in terms of metric such as a (relatively) large positive Lyapunov exponent, high information entropy, a high cycle length and key diffusion characteristics, for example. This approach provides the potential for generating an unlimited number of unique Pseudo Random Number Generator (PRNG) that can be used on a I-to-l basis. Typical applications include the encryption of data before it is uploaded onto the Cloud by a user that is provided with a personalized encryption algorithm rather than just a personal key using a 'known algorithm' that may be subject to a ' known algorithm attack' and/or is 'open' to the very authorities who are promoting its use.
This paper presents and discusses a method of generating encryption algorithms using neural networks and evolutionary computing. Based on the application of natural noise sources obtained from data that can include at...
详细信息
ISBN:
(纸本)9781479919611
This paper presents and discusses a method of generating encryption algorithms using neural networks and evolutionary computing. Based on the application of natural noise sources obtained from data that can include atmospheric noise (generated by radio emissions due to lightening, for example), radioactive decay, electronic noise and so on, we 'teach' a system to approximate the input noise with the aim of generating an output nonlinear function. This output is then treated as an iterator which is subjected to a range of tests to check for potential cryptographic strength in terms of metric such as a (relatively) large positive Lyapunov exponent, high information entropy, a high cycle length and key diffusion characteristics, for example. This approach provides the potential for generating an unlimited number of unique Pseudo Random Number Generator (PRNG) that can be used on a 1-to-1 basis. Typical applications include the encryption of data before it is uploaded onto the Cloud by a user that is provided with a personalized encryption algorithm rather than just a personal key using a 'known algorithm' that may be subject to a 'known algorithm attack' and/or is 'open' to the very authorities who are promoting its use.
We present a method of generating encryptors, in particular, Pseudo Random Number Generators (PRNG), using evolutionary computing. Working with a system called E_(ureqa), designed by the Cornell Creative Machines Lab,...
详细信息
ISBN:
(纸本)9781629935881
We present a method of generating encryptors, in particular, Pseudo Random Number Generators (PRNG), using evolutionary computing. Working with a system called E_(ureqa), designed by the Cornell Creative Machines Lab, we seed the system with natural noise sources obtained from data that can include atmospheric noise generated by radio emissions due to lightening, for example, radioactive decay, electronic noise and so on. The purpose of this is to 'force' the system to output a result (a nonlinear function) that is an approximation to the input noise. This output is then treated as an iterated function which is subjected to a range of tests to check for potential cryptographic strength in terms of a positive Lyapunov exponent, maximum entropy, high cycle length, key diffusion characteristics etc. This approach provides the potential for generating an unlimited number of unique PRNG that can be used on a 1-to-1 basis. Typical applications include the encryption of data before it is uploaded onto the Cloud by a user that is provided with a personalised encryption algorithm rather than just a personal key using a 'known algorithm' that may be subject to a 'known algorithm attack' and/or is 'open' to the very authorities who are promoting its use.
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