{"title":"Attack Graph-Based Data Presentation for Use in Automated Security Analysis Systems","authors":"E. M. Orel, D. A. Moskvin, A. A. Lyashenko","doi":"10.3103/S0146411624701232","DOIUrl":"10.3103/S0146411624701232","url":null,"abstract":"<p>A mathematical model for data representation is developed for use in automated security analysis systems. The model allows linking information about the system obtained by a specialist during the security analysis process with the set of attack scenarios in which they may be involved. Each time a scenario is applied, new information emerges, which helps expand the attack graph.</p>","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"58 8","pages":"1436 - 1441"},"PeriodicalIF":0.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Intelligent Recommendation System for Countering Network Attacks","authors":"I. A. Goretskii, D. S. Lavrova","doi":"10.3103/S0146411624701050","DOIUrl":"10.3103/S0146411624701050","url":null,"abstract":"<p>This paper studies an approach to counteract network attacks based on network reconfiguration to eliminate the possibility of the successful completion of an attack by an intruder. To implement this approach, it is proposed to use a recommender system mechanism that provides both the generation of possible network topologies and their ranking. The proposed intelligent recommendation system is based on a reinforcement learning algorithm based on the actor-critic model. The conducted experimental studies confirm the effectiveness of the developed system.</p>","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"58 8","pages":"1386 - 1391"},"PeriodicalIF":0.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Protection Against Unauthorized Access of Mobile Devices When Using the BYOD Concept","authors":"A. A. Kornienko, S. V. Kornienko, N. S. Razzhivin","doi":"10.3103/S0146411624700998","DOIUrl":"10.3103/S0146411624700998","url":null,"abstract":"<p>The problems of using mobile devices in the work process of an organization when applying the BYOD concept are analyzed. An adapted methodology for assessing security threats is proposed for BYOD concepts. As a supplement to the traditional approach to building a system for ensuring information security in a corporate information system based on BYOD, a software tool for controlling unauthorized access to mobile devices is developed and tested.</p>","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"58 8","pages":"1326 - 1335"},"PeriodicalIF":0.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. A. Poltavtseva, A. A. Podorov, E. B. Aleksandrova
{"title":"Verification of Access Control in Big Data Systems Using Temporal Logics","authors":"M. A. Poltavtseva, A. A. Podorov, E. B. Aleksandrova","doi":"10.3103/S0146411624700974","DOIUrl":"10.3103/S0146411624700974","url":null,"abstract":"<p>Ensuring consistent access control is one of the key security challenges in heterogeneous big data systems. The problem is the large number of data processing tools, as well as sources and users of information; heterogeneity of security models; and complexity of granular access rules. Analysis of the time factor in this case will improve the consistency and reliability of access control. The aim of this study is to select a methodology and tools for implementing temporal logic in the processes of access control verification of big data systems. The types of temporal logic and verification methods based on the temporal logic of actions (TLAs) are analyzed. The use of TLA+ is proposed to solve the given problem and an example of the corresponding specification is given.</p>","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"58 8","pages":"1311 - 1317"},"PeriodicalIF":0.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. B. Aleksandrova, A. I. Bezborodko, D. S. Lavrova
{"title":"The Use of Generative-Adversarial Networks to Counter Steganalysis","authors":"E. B. Aleksandrova, A. I. Bezborodko, D. S. Lavrova","doi":"10.3103/S0146411624700937","DOIUrl":"10.3103/S0146411624700937","url":null,"abstract":"<p>An approach using a generative adversarial network (GAN) is proposed to increase the robustness of the steganographic method against modern steganalyzers. This approach is based on the combined operation of a GAN, a pixel importance map, and the least significant bit (LSB) substitution method. The results of the experimental studies confirmed the effectiveness of the proposed approach.</p>","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"58 8","pages":"1283 - 1288"},"PeriodicalIF":0.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"On the Method of Masking in the Multiple Signature Protocol Based on Isogenies of Elliptic Curves","authors":"E. B. Aleksandrova, S. O. Kostin","doi":"10.3103/S0146411624700950","DOIUrl":"10.3103/S0146411624700950","url":null,"abstract":"<p>Among the post-quantum algorithms selected by the National Institute of Standards and Technology (NIST) for standardization, the main mathematical apparatus is the mechanism of algebraic lattices, while the apparatus of hash functions is an alternative. Unlike isogenies of elliptic curves, these mechanisms use larger sizes of both public keys and signatures. Using the example of a multiple signature protocol based on isogenies of elliptic curves, we will show how, using the masking method, we can protect against the main attack on the given device, while obtaining a smaller signature size.</p>","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"58 8","pages":"1297 - 1302"},"PeriodicalIF":0.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. N. Ruchkin, V. A. Fulin, E. V. Ruchkina, D. V. Grigorenko
{"title":"Cluster Analysis of a Collective of Algorithms of Multicore Neural Network Automata and Robots on Chip","authors":"V. N. Ruchkin, V. A. Fulin, E. V. Ruchkina, D. V. Grigorenko","doi":"10.3103/S0146411624700780","DOIUrl":"10.3103/S0146411624700780","url":null,"abstract":"<p>In view of improved performance of new spheres and directions of social development, Russian government pays attention to robotization based on modern Russian hardware elements for the purpose of import substitution. In this regard, association of the notions of collective of algorithms, collective of automata, collective of robots, and artificial intelligence is one of the topical problems. Of special importance are the capabilities of cybernetic investigation of multicore neural network automata for the purpose of constructing more complex automata, robots, and the behavior of a collective of robots on their basis. The current paper is aimed at demonstrating the capabilities of the set-theoretic cybernetic approach to artificial and complex natural objects and systems on their basis and creating a conceptual model of selection and joint simultaneous design of hardware and software tools of neural network automata on the basis of unified study of parallelization processes in a collective of automata in the form of explicit and implicit clusterization. The options of structures of collective of algorithms for providing cybersecurity and protection against threats in the form of hierarchy of security provision practices are analyzed, shown, and proposed. The paper proposes a method for analyzing and selecting the best architecture of a multicore neural network collective of automata and a collective of robots based on automata implemented on chip. An expert system based on VLSI 1879VM8Ya (NM6408) with a developed user interface is implemented.</p>","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"58 8","pages":"1156 - 1163"},"PeriodicalIF":0.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Researching the Performance of AutoML Platforms in Confidential Computing","authors":"S. V. Bezzateev, G. A. Zhemelev, S. G. Fomicheva","doi":"10.3103/S0146411624701049","DOIUrl":"10.3103/S0146411624701049","url":null,"abstract":"<p>The paper is dedicated to testing the performance indicators of automatic machine learning platforms when they function in standard and confidential modes using the example of a nonlinear multidimensional regression. A general protocol of distributed machine learning trusted in the sense of security is proposed. It is shown that within the framework of confidential virtualization, when optimizing the architecture of machine learning pipelines and hyperparameters, the best quality indicators of generated pipelines for multidimensional regressors and speed characteristics are demonstrated by solutions based on Auto Sklearn compared with Azure AutoML, which is explained by different learning strategies. The results of the experiments are presented.</p>","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"58 8","pages":"1373 - 1385"},"PeriodicalIF":0.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. O. Kalinin, A. G. Busygin, A. S. Konoplev, V. M. Krundyshev
{"title":"Application of Distributed Ledger Technology to Ensure the Security of Smart City Information Systems","authors":"M. O. Kalinin, A. G. Busygin, A. S. Konoplev, V. M. Krundyshev","doi":"10.3103/S0146411624701001","DOIUrl":"10.3103/S0146411624701001","url":null,"abstract":"<p>This article discusses the ways of using distributed ledger technology to ensure the security of smart city information systems. The authors outline the limitations of the existing solutions in this area and the main directions of development of distributed ledger technology, determining its successful integration into the smart city ecosystem.</p>","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"58 8","pages":"1336 - 1342"},"PeriodicalIF":0.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Detecting Attacks Using Artificial Neural Networks","authors":"I. A. Sikarev, T. M. Tatarnikova","doi":"10.3103/S0146411624700858","DOIUrl":"10.3103/S0146411624700858","url":null,"abstract":"<p>The developed neural network attack detection algorithm, whose peculiarity lies in the possibility of launching two parallel processes, is described: searching for the optimal model of an artificial neural network and normalization of the training sample data. It is shown that the artificial neural network architecture is selected taking into account the loss function for a limited set of attack classes. The application of TensorFlow and Keras Tuner libraries (frameworks) for the software implementation of an attack detection algorithm is shown. An experiment on the selection of neural network architecture and its training is described. The accuracy obtained in experiments is 94–98% for different classes of attacks.</p>","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"58 8","pages":"1218 - 1225"},"PeriodicalIF":0.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}