Title: Improved genetic algorithms for solving the optimisation tasks for design of access control schemes in computer networks

Authors: Igor Kotenko; Igor Saenko

Addresses: Laboratory of Computer Security Problems, St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), 39, 14 Liniya, St. Petersburg, 199178, Russia; St. Petersburg National Research University of Information Technologies, Mechanics and Optics, 49, Kronverkskiy prospekt, St. Petersburg, Russia ' Laboratory of Computer Security Problems, St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), 39, 14 Liniya, St. Petersburg, 199178, Russia

Abstract: Access control scheme design is the most important task in the field of computer network security, which has to be solved by security administrators and developers. The access control quality strongly affects such important security properties, as information privacy and accessibility. One of the solutions to this problem is to reduce it to a form of the optimisation task and its subsequent solving by mathematical methods. However, due to the large complexity of this task, applying traditional mathematical methods is very difficult. At the same time, genetic algorithms represent a new and very interesting way to solve this class of problems. This paper suggests an approach for designing access control schemes based on genetic algorithms. To enhance the implementation of genetic operations it proposes a number of significant improvements, which include the multi-chromosomal representation of individuals in populations, the usage of complex data types to represent genes in chromosomes and the use of special control chromosomes. The experimental evaluation of the approach is discussed. It is demonstrated that the proposed improved genetic algorithms are quite efficient means for access control schemes optimisation in computer networks.

Keywords: genetic algorithms; access control; optimisation; network security; computer networks.

DOI: 10.1504/IJBIC.2015.069291

International Journal of Bio-Inspired Computation, 2015 Vol.7 No.2, pp.98 - 110

Received: 05 Aug 2014
Accepted: 10 Dec 2014

Published online: 08 May 2015 *

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