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Purpose of the article: development of mechanisms for solving problems of information risk management of complex systems in conditions of uncertainty and mutual influence of system elements on each other.Research method: game-theoretic mathematical modeling of risk management processes in complex systems based on arbitration schemes and multistep games on cognitive maps.The result: a general model of a complex system (for example, a heterogeneous computer network) is considered, within which the risk manager (risk-manager) carries out effective risk management by distributing the resource at his disposal among its elements (nodes of a computer network). To assess the state of the system elements, functions of local risk are proposed that satisfy certain specified requirements, and to assess the state of the system as a whole, an integral risk function is proposed.It is shown that in the case of independence (absence of mutual influence on each other) of the system elements to find an effective resource allocation, a game-theoretic approach can be used based on an arbitration scheme based on the principles of stimulation and non-suppression (MS-solution).For the case when changes in the level of risk for one element of the system can have a significant impact on the levels of risks of other elements, it is proposed to use game-theoretic models based on the MS-solution and a multistep “cognitive game”.
Keywords: local risk, integral risk, risk manager, game-theoretic models, arbitration scheme, maximally stimulating decision, cognitive map, multistep cognitive game.

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The purpose of the article: comparative analysis of methods for solving various cybersecurity problems based on the use of deep learning algorithms.Research method: Systematic analysis of modern methods of deep learning in various cybersecurity applications, including intrusion and malware detection, network traffic analysis, and some other tasks. The result obtained: classification scheme of the considered approaches to deep learning in cybersecurity, and their comparative characteristics by the used models, characteristics, and data sets. The analysis showed that many deeper architectures with a large number of neurons on each layer show better results. Recommendations are given for using deep learning methods in cybersecurity applications.The main contribution of the authors to the research of deep learning methods for cybersecurity tasks is the classification of the subject area; conducting a general and comparative analysis of existing approaches that reflect the current state of scientific research.
Keywords: data science, machine learning, deep neural networks, intrusion detection, malware detection.

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Purpose: the development of automated system of software vulnerabilities analysis for information-control systems on the basis of intelligent analysis of texts written on the natural language (Text Mining).
Methods: the idea of the used investigation method is based on matching the set of extracted software vulnerabilities and relevant information security threats by means of evaluating the semantic similarity metrics of their textual description with use of Text Mining methods. Practical relevance: the architecture of the automated system of software vulnerabilities analysis is developed, the application of which allows us to evaluate the level of vulnerabilities criticality and match it with the most suitable by discretion (i.e. semantically similar) threats from the Bank of information security threats of FSTEC Russia while ensuring vulnerabilities and threats. The main software modules of the system have been developed. Computational experiments were carried out to assess the effectiveness of its application. The results of comparative analysis show that application of the given system allows us to increase the credibility of evaluating the criticality degree of vulnerabilities, considerably decreasing the time for a search and matching vulnerabilities and threats.
Keywords:  information security threats, intelligent filtering, vector word representation, lemmatization, semantic proximity.

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Objective. Service-oriented architecture and its microservice-based approach increase an attack surface of applications. Exposed microservices become a pivot point for advanced persistent threats and completely change the threat landscape. Correctly implemented authentication and authorization architecture patterns are basis of any software maturity program. The aim of this study is to provide a helpful resource to application security architect and developers on existing architecture patterns to implement authentication and authorization in microservices-based systems.Method. In this paper, we conduct a systematic review of major electronic databases and libraries as well as security standards and presentations at the major security conferences.Results and practical relevance. In this work based on research papers and major security conferences presentations analysis, we identified industry best practices in authentication and authorization patterns and its applicability depending on environment characteristic. For each described patterns we reviewed its advantages and disadvantages that could be used as decision-making criteria for application security architects during architecture design phase.
Keywords: microservice architectures, security, authentication, authorization, architecture patterns survey.

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22. Sheluhin O.I., Ryabinin V.S., Farmakovskiy M.А., Anomaly detection in computer system by intellectictual analysis of system journals, Voprosy kiberbezopasnosti, №2(26), 2018. p 36-41. (in Russ.) DOI: 10.21681/2311-3456-2018-2-33-43

The purpose: to construct an algorithm for information transformation by recurrent convergent neural networks with a given set of local minima of the energy functional for its subsequent application in the field of information security.Method: system analysis of the existing neural network paradigms that can be used for classification of images. Neural cryptographic system synthesis is with analogy methods, recurrent convergent neural networks, noise-resistant encoding and block ciphers algorithms.The result: a promising neural cryptographic system is proposed that can be used to develop an algorithm for noise-resistant coding, symmetric or stream data encryption based on the generation of various variants of the distorted image representing the sequence of bits to mask the original message. An algorithm for block symmetric data encryption based on Hopfield-type neural networks has been created. Key information includes information on the selected (using radial basic functions) structural characteristics of the potential with a given set of energy minima, which determines the dynamics of the neural network as a potential dynamic system, whose attractors are symbols (several symbols) of the alphabet of the input text. The size of the key depends on the power of the alphabet of the original message and the form of representation of the energy functional. The presented neural cryptographic system can also be used in the authentication system.
Keywords: neural cryptography, noise-resistant coding, symmetric encryption, a block cipher, authentication system, neural network with feedback, potential dynamical system, radial base function.

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Every year, the technology of using smart contracts is attracting more and more attention from users due to the unique advantages that it possesses: automatic execution of transactions in a traceable and unchanging way without third party authorization. At the same time, a smart contract is one of the most vulnerable elements in distributed ledger systems, which can be susceptible to attack by intruders.The aim of the research is to develop a methodology that allows analyzing a smart contract for information security vulnerabilities and determining the security level of a smart contract before placing it in distributed ledger systems.Research methods: to achieve this goal, methods of static and dynamic analysis were studied, the most relevant information security vulnerabilities were identified, and parameters for calculating the criticality factor of vulnerability and the security level of a smart contract were determined.Result: a promising static-dynamic method for analyzing the vulnerabilities of a smart contract is proposed, which makes it possible to unambiguously determine the security level of a smart contract before its placement in the distributed ledger system. Its main parameters are set, and the reference security factors of a smart contract are determined. The complete algorithm of the static-dynamic method of analyzing a smart contract is described, and an example of a generated documentary security report based on the results of analyzing a smart contract is given.
Keywords: static analysis, dynamic analysis, automated tools, information security, vulnerability criticality, testing, code audit.

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The purpose of the study is to analyze the existing requirements for personal data security and assess the impact of these requirements on the enterprises security in the Russian Federation.Research method: the problem of ensuring the security of personal data in accordance with the requirements of the Federal law of the Russian Federation FZ-152 and the international General Data Protection Regulation is investigated. The article analyzes the possible risks of interrupting the normal activities of enterprises in the Russian Federation due to violations of these requirements for personal data protection and the imposition of significant fines by international regulators. Numerical relationships are estimated between the amount of fines for violations of established requirements, including General Data Protection Regulation, and the cost of creating an effectiveness personal data protection system. Estimates of the permissible degree of influence of the General Data Protection Regulation requirements on the enterprises security in the Russian Federation are obtained.Research result: a study and comparison of possible penalties for violation of compliance with the requirements of the Federal law of the Russian Federation FZ-152 and the international General Data Protection Regulation was performed. Risk assessments of sanctions for violation of the established requirements for personal data protection were obtained. The analysis of the cost of preparing a personal data protection system for compliance with the requirements of the General Data Protection Regulation was performed. Based on the data obtained, examples of calculating the degree of maturity of the security system are presented - based on the ratio of the share of the budget allocated for security in relation to the cost of creating an effectiveness personal data protection system and based on the ratio of the amount of the fine for violation of the established requirements. The importance of accounting for the costs of personal data security to ensure the security of enterprises in the Russian Federation, taking into account the requirements of the General Data Protection Regulation, is shown.
Keywords: personal data, risk assessment, budget, damage, Federal law, threat, level of maturity, system, conformity assessment.

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The purpose of the study: development of a technique for determining lexical characteristics and psycholinguistic factors as discriminative features for identifying the topics of illegal texts by frequency methods for information security purposes.Method: automatic morphological and syntactic analysis, frequency methods, comparison of auto-generated dictionaries by correlation analysis methods.Results: a technique of frequency analysis of the illegal texts vocabulary has been developed, which allows to compare different sets of texts using frequency dictionaries and identify discriminative features; a technique of calculating pairwise rank correlation coefficient for comparison of frequency dictionaries of various lexical characteristics has been presented; a comparative analysis of different illegal texts collections has been carried out; the possibility of using frequency lexical characteristics to study the properties of texts in order to detect illegal resources and messages has been shown; the possibilities of using both morphological characteristics of words and word combinations and letter combinations as discriminative features have been shown; the possibility of calculating the psycholinguistic indicators of illegal texts based on automatic linguistic text analysis has been shown; the psycholinguistic characteristics for texts of various topics have been highlighted.
Keywords: automated text analysis, noun phrases, rank correlation, psycholinguistics characteristics, extremist
texts.

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