FUNCTIONAL MODEL OF CLASSIFICATION OF PSEUDO-RANDOM SEQUENCES OF ENCRYPTED AND COMPRESSED DATA PREVENTION OF LEAKAGE OF CONFIDENTIAL INFORMATION
DOI:
https://doi.org/10.17721/2519-481X/2023/81-09Keywords:
pseudorandom sequences, functional model, information security, classification accuracy, encrypted, compressed dataAbstract
The task of building a formalized insider model, which can be used both in commercial and public companies, is considered. It is shown that data security threats are characterized by a set of qualitative and quantitative vector indicators, and their formalization requires the application of fuzzy set theory and discrete mathematics. It is shown that it is impossible to use expert traditional assessment methods to determine most of the considered indicators.
To minimize the risk of leakage of confidential information, it is suggested to form groups of employees and calculate the risk of leakage of confidential data for each of them.
The development of a model of pseudo-random sequences will allow us to assess the degree of influence of statistical features extracted from pseudo-random sequences and used in the process of forming a classifier on the accuracy of the classification procedure. The obtained quantitative values of the features will allow to optimize the number of parameters, subject to the required accuracy, to estimate the complexity of the feature removal procedure. On the basis of the simulation results obtained, the identified features of the classifier, it is necessary to justify the choice of a mathematical apparatus, which will allow us to proceed to the practical implementation of the sequence classification algorithm formed by data compression and encryption algorithms.
The conducted analysis of research in this subject area made it possible to identify a practical problem of existing protection mechanisms: low accuracy of detecting encrypted information, due to their similarity to typical high-entropy sequences, use of service information inherent in the transmission process, storage of confidential information. Thus, the task of classifying encrypted and compressed data is relevant.
In order to solve the given task, it is necessary to: conduct an analysis of the features of the functioning of prospective means of preventing and detecting the leakage of confidential data, identify the limitations associated with the detection of compressed and encrypted information, justify the choice of an appropriate feature space for modeling pseudo-random sequences formed by information compression and encryption algorithms; to develop a model of pseudo-random sequences formed by data compression and encryption algorithms, which differs from known ones, taking into account their statistical characteristics.
The presented model of pseudorandom sequences differs from analogs taking into account the distribution of bytes and taking into account the frequencies of bit subsequences of length 9 bits. To assess the adequacy of the proposed model, experiments were conducted to determine the accuracy of classification of pseudorandom sequences by machine learning algorithms.
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