A combined genetic optimization and multilayer perceptron methodology for efficient digital fingerprint modeling and evaluation in secure communications

A novel procedure based on genetic algorithms and multilayer perceptron (MLP) neural networks (NN) is presented for the evaluation and production of digital fingerprints in the design of secure communication systems. These digital fingerprints are computed using the methodology of un-keyed one-way functions (hash functions). The problem of evaluating the quality of such functions is formulated as a global optimization one in the space spanned by all possible messages and is approached from a practical viewpoint by involving genetic algorithms and MLP neural networks, contrary to the very few similar research efforts existing in the literature that are of only theoretical interest. Moreover, the problem of producing digital fingerprints of good quality for use in communication systems is formulated in terms of a hash function constructed by involving the genetic algorithm procedure, exclusively utilizing the crossover operator and the steady-state reproduction method and omitting its random components, as well as by involving a suitably designed MLP NN. An extensive experimental study is conducted to assess the quality of the results produced by applying the herein suggested two novel approaches, employing SHA, the known one way hash function algorithm, in the comparison procedure. The promising results herein obtained illustrate the importance of applying genetic algorithms and neural networks in communication systems security design.