4 resultados para cord compression

em Bulgarian Digital Mathematics Library at IMI-BAS


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In this paper a possibility for quantitative measuring of information security of objects, exposed to information attacks and processed with methods of compression, is represented. A co-efficient of information security, which reflects the influence of the level of compression obtained after applying methods of compression to objects and the time, required by the attack to get access to the corresponding object, is proposed. Methods’ groups with the highest and respectively the lowest values of the co-efficient of information security for all methods’ groups in relation to all attacks’ groups are determined. Assessments and conclusions for future investigations are proposed.

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In this paper is proposed a model for researching the capability to influence, by selected methods’ groups of compression, to the co-efficient of information security of selected objects’ groups, exposed to selected attacks’ groups. With the help of methods for multi-criteria evaluation are chosen the methods’ groups with the lowest risk with respect to the information security. Recommendations for future investigations are proposed.

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In this paper a methodology for evaluation of information security of objects under attacks, processed by methods of compression, is represented. Two basic parameters for evaluation of information security of objects – TIME and SIZE – are chosen and the characteristics, which reflect on their evaluation, are analyzed and estimated. A co-efficient of information security of object is proposed as a mean of the coefficients of the parameter TIME and SIZE. From the simulation experiments which were carried out methods with the highest co-efficient of information security had been determined. Assessments and conclusions for future investigations are proposed.

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questions of forming of learning sets for artificial neural networks in problems of lossless data compression are considered. Methods of construction and use of learning sets are studied. The way of forming of learning set during training an artificial neural network on the data stream is offered.