Abstract
One of possible levels of computer protection may consist in splitting computer networks into logical chunks that are known as virtual computer networks or virtual subnets. The paper considers a novel approach to determine virtual subnets that is based on the given matrix of logic connectivity of computers. The paper shows that the problem considered is related to one of the forms of Boolean Matrix Factorization. It formulates the virtual subnet design task and proposes genetic algorithms as a means to solve it. Basic improvements proposed in the paper are using trivial solutions to generate an initial population, taking into account in the fitness function the criterion of minimum number of virtual subnets, and using columns of the connectivity matrix as genes of chromosomes. Experimental results show the proposed genetic algorithm has high effectiveness.
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Catalyst 2900 Series XL and Catalyst 3500 Series XL Software Configuration Guide. Cisco IOS Release 12.0(5) WC(1). Cisco Systems, San Jose (2001)
Miettinen, P., Vreeken, J.: Model Order Selection for Boolean Matrix Factorization. In: 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, New York (2011)
Miettinen, P.: Dynamic Boolean Matrix Factorizations. In: 2012 IEEE 12th International Conference on Data Mining. ACM, New York (2012)
Cergani, E., Miettinen, P.: Discovering Relations using Matrix Factorization Methods. In: 22nd ACM International Conference on Information & Knowledge Management. ACM, New York (2013)
Lu, H., Vaidya, J., Atluri, V., Hong, Y.: Extended Boolean Matrix Decomposition. In: Ninth IEEE International Conference on Data Mining. IEEE Press, New York (2009)
Lu, H., Vaidya, J., Atluri, V.: Optimal Boolean Matrix Decomposition: Application to Role Engineering. In: 24th IEEE International Conference on Data Engineering. IEEE Press, New York (2008)
Saenko, I., Kotenko, I.: Genetic Algorithms for Role Mining Problem. In: 19th International Euromicro Conference on Parallel, Distributed and Network-Based Processing. IEEE Press, New York (2011)
Saenko, I., Kotenko, I.: Design and Performance Evaluation of Improved Genetic Algorithm for Role Mining Problem. In: 20th International Euromicro Conference on Parallel, Distributed and Network-based Processing. IEEE Press, New York (2011)
Janecek, A., Tan, Y.: Using Population Based Algorithms for Initializing Nonnegative Matrix Factorization. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011, Part II. LNCS, vol. 6729, pp. 307–316. Springer, Heidelberg (2011)
Snasel, V., Platos, J., Kromer, P.: On Genetic Algorithms for Boolean Matrix Factorization. In: Eighth International Conference on Intelligent Systems Design and Applications, vol. 2, pp. 170–175. IEEE Press, New York (2008)
Snasel, V., Platos, J., Kromer, P., Husek, D., Neruda, R., Frolov, A.A.: Investigating Boolean Matrix Factorization. In: Workshop on Data Mining using Matrices and Tensors (2008)
Tai, C.-F., Chiang, T.-C., Hou, T.-W.: A Virtual Subnet Scheme on Clustering Algorithms for Mobile Ad Hoc Networks. Expert Systems with Applications 38(3), 2099–2109 (2011)
Saenko, I., Kotenko, I.: Genetic Optimization of Access Control Schemes in Virtual Local Area Networks. In: Kotenko, I., Skormin, V. (eds.) MMM-ACNS 2010. LNCS, vol. 6258, pp. 209–216. Springer, Heidelberg (2010)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Longman Publishing, Boston (1989)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Massachusetts (1998)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer (2007)
Barker, E., Kelsey, J.: Recommendation for Random Number Generation Using Deterministic Random Bit Generators. NIST Special Publication. NIST (2012)
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Saenko, I., Kotenko, I. (2015). A Genetic Approach for Virtual Computer Network Design. In: Camacho, D., Braubach, L., Venticinque, S., Badica, C. (eds) Intelligent Distributed Computing VIII. Studies in Computational Intelligence, vol 570. Springer, Cham. https://doi.org/10.1007/978-3-319-10422-5_11
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DOI: https://doi.org/10.1007/978-3-319-10422-5_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10421-8
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