9004 2071-8217 1 2025 1-164

RAR RUS 9-20 0009-0007-7981-1146 Rostov State University of Economics Chizhevsky Maxim ch.inc@yandex.ru

Russia, 344000, Rostov-on-Don, Bolshaya Sadovaya str., 69

0000-0002-5583-4972 Rostov State University of Economics Serpeninov Oleg serpeninov53@mail.ru

Russia, 344000, Rostov-on-Don, Bolshaya Sadovaya str., 69

0000-0003-2273-725X Rostov State University of Economics Lapsar Aleksey lapsar1958@mail.ru

Russia, 344000, Rostov-on-Don, Bolshaya Sadovaya str., 69

Optimization of indicator of compromise utilization in information security tasks The article deals with the problem of updating indicators of compromise in the field of information security. One of the key difficulties is the growing number of false positives, which slows down the process of incident investigation. To solve this problem, we propose a model for assessing the relevance of indicators of compromise, the purpose of which is to optimise their use. The developed model takes into account various parameters, such as the indicator obsolescence rate, the level of trust in the source, the frequency of detection, the proportion of false positives, the consideration of information from open sources, and the type of malicious activity. The model reduces the number of false positives and improves the efficiency of incident monitoring. 10.48612/jisp/t99x-zeux-75er 004.056 Indicator of compromise relevance assessment model relevance dynamics information security https://jisp.spbstu.ru/article/2025.20.1/ 2025_1-5-6.pdf

RAR RUS 21-29 Peter the Great St. Petersburg Polytechnic University Bezborodov Pavel bezborodov_pd@edu.spbstu.ru

Russia, 195251, St. Petersburg, Polytechnicheskaya str., 29

0000-0003-2849-4682 Peter the Great St. Petersburg Polytechnic University Lavrova Daria lavrova_ds@spbstu.ru

Russia, 195251, St. Petersburg, Polytechnicheskaya str., 29

Protecting neural network models from privacy violation threats in federated learning using optimization methods The paper is devoted to an approach to counter threats of privacy violations in federated learning. The approach is based on optimization methods to transform the weights of local neural network models and create new weights for transmission to the joint gradient descent node, which, in turn, allows to prevent the interception of local model weights by an attacker. Experimental studies have confirmed the effectiveness of the developed approach. 10.48612/jisp/fpvk-xpna-9hx5 004.056 Federated learning neural network models optimization methods gradient descent https://jisp.spbstu.ru/article/2025.20.2/ 2025_1-5-6.pdf

RAR RUS 30-42 0000-0003-1300-2470 Mozhaysky Military Space Academy Biryukov Denis Biryukov.D.N@yandex.ru

Russia, 197198, St. Petersburg, Zhdanovskaya str., 13

0000-0001-9665-0128 Peter the Great St. Petersburg Polytechnic University Suprun Alexander afs54@inbox.ru

Russia, 195251, St. Petersburg, Polytechnicheskaya str., 29

From “black box” to transparency: philosophical and methodological foundations of explainability and interpretability in artificial intelligence This article examines the problem of the «black box» in artificial intelligence systems, focusing on the role of explanation (revealing cause-and-effect relationships) and interpretation (adapting meaning for the audience) in the context of machine learning. The philosophical foundations of these concepts are presented, along with an overview of modern methods in explainable AI (XAI). The article emphasizes the need to develop common perspectives on the issues of “explainability” and “interpretability” as they apply to machine learning models and the solutions they generate. 10.48612/jisp/x8ve-86ez-fv94 004.8 + 004.9 + 165.12 Artificial intelligence explanation interpretation understanding XAI https://jisp.spbstu.ru/article/2025.20.3/ 2025_1-5-6.pdf

RAR RUS 43-58 0009-0005-6662-5606 Saint Petersburg State University of Aerospace Instrumentation Velichko Ivan wwr0ngn4m3@gmail.com

Russia, 190000, St. Petersburg, Bolshaya Morskaya str., 67, liter A

0000-0002-0924-6221 Saint Petersburg State University of Aerospace Instrumentation Bezzateev Sergey sergey.bezzateev@gmail.com

Russia, 190000, St. Petersburg, Bolshaya Morskaya str., 67, liter A

From exploitation to protection: a deep dive into adversarial attacks on LLMS Modern large language models possess impressive capabilities but remain vulnerable to various attacks that can manipulate their responses, lead to leakage of confidential data, or bypass restrictions. This paper focuses on the analysis of prompt injection attacks, which allow bypassing model constraints, extracting hidden data, or forcing the model to follow malicious instructions. 10.48612/jisp/mbvv-n1u7-z7be 004.056.52 Large language models artificial intelligence adversarial attacks defense methods model output manipulation https://jisp.spbstu.ru/article/2025.20.4/ 2025_1-5-6.pdf

RAR RUS 59-68 Peter the Great St. Petersburg Polytechnic University Kirillov Roman kirillov.rb@edu.spbstu.ru

Russia, 195251, St. Petersburg, Polytechnicheskaya str., 29

0000-0002-9732-0099 Peter the Great St. Petersburg Polytechnic University Kalinin Maxim max@ibks.spbstu.ru

Russia, 195251, St. Petersburg, Polytechnicheskaya str., 29

Detecting adversarial samples in intrusion detection systems using machine learning models The problem of protecting machine learning models used in intrusion detection systems from adversarial attacks is considered. Possible methods of protection against adversarial samples based on data anomaly detectors and an autoencoder are analyzed. The results of an experimental study of protective mechanisms that demonstrated high efficiency in detecting distorting data using a Random Forest model are presented. 10.48612/jisp/2741-bb1k-hf3x 004.056 Adversarial attack machine learning security adversarial sample detection machine learning intrusion detection system Random Forest https://jisp.spbstu.ru/article/2025.20.5/ 2025_1-5-6.pdf

RAR RUS 69-82 0009-0002-7321-7430 Peter the Great St. Petersburg Polytechnic University Bugaev Vyacheslav bugaev.va@edu.spbstu.ru

Russia, 195251, St. Petersburg, Polytechnicheskaya str., 29

0009-0002-7321-7430 Peter the Great St. Petersburg Polytechnic University Zhukovskii Evgeniy bugaev.va@edu.spbstu.ru

Russia, 195251, St. Petersburg, Polytechnicheskaya str., 29

0009-0008-4593-4444 Peter the Great St. Petersburg Polytechnic University Lyrchikov Alexander lyrchikov.aa@gmail.com

Russia, 195251, St. Petersburg, Polytechnicheskaya str., 29

Detection of potentially malicious activity in CI/CD pipelines based on analysis of runner behavior The article addresses the problem of detecting potentially malicious activity in CI/CD pipelines during the build process through the analysis of runner behavior. The limitations of existing pipeline security tools related to threat detection during build execution are identified, as well as promising approaches to detecting malicious activity. A way for detecting potentially malicious activity in pipelines using the eBPF technology for collecting and analyzing runner behavior has been proposed. The accuracy of the detection is evaluated using a dataset that contains implementations of malicious scenarios related to build process compromise. The results obtained can be used to implement protection tools for CI systems and contribute to research in CI/CD pipelines security. 10.48612/jisp/at5b-46tf-zet9 004.056 CI/CD pipelines DevSecOps malicious activity anomaly detection eBPF behavioral analysis syscalls https://jisp.spbstu.ru/article/2025.20.6/ 2025_1-5-6.pdf

RAR RUS 83-96 0000-0002-1764-1942 A. F. Mozhaysky Military Space Academy Lomako Alexander 0009-0007-2797-6133 A. F. Mozhaysky Military Space Academy Isaev Nail 0000-0002-9955-2694 A. F. Mozhaysky Military Space Academy Menisov Artem 0000-0002-6807-2954 A. F. Mozhaysky Military Space Academy Sabirov Timur An approach to identifying software code vulnerabilities based on adaptation with reinforcement learning of machine learning models The article is devoted to the development of an approach to identifying vulnerable code using adaptation methods for pre-trained reinforcement machine learning models. A training methodology is presented that includes stages of model adaptation using data from various domains, which ensures high generalization ability of the algorithms. Experimental results have shown the effectiveness of the proposed approach on the popular CWEFix code analysis dataset. The developed approach helps to improve the quality of vulnerability detection and reduce the level of false positives, which makes it a useful tool for ensuring software security. 10.48612/jisp/7gnx-9z7f-fbrv 004.9 Code vulnerabilities machine learning reinforcement learning software analysis information security https://jisp.spbstu.ru/article/2025.20.7/ 2025_1-5-6.pdf

RAR RUS 97-105 0009-0000-3181-4769 Peter the Great St. Petersburg Polytechnic University Kostin Sergey s8kostin@gmail.com 0000-0002-7485-4848 Peter the Great St. Petersburg Polytechnic University Aleksandrova Elena aleksandrova_eb@spbstu.ru Multiple signatures on elliptic curve isogenies with masking and participant authentication This work investigates approaches for constructing post-quantum digital signature schemes. Contemporary methods for enhancing the security of protocols based on elliptic curve isogenies are analyzed. Multi-signature scheme based on the problem of finding isogenies between supersingular curves with participant authentication is developed. The efficiency and security of the proposed scheme are proved. 10.48612/jisp/xvpd-hah6-9a56 004.056.53 Group signature supersingular elliptic curves postquantum cryptography masking https://jisp.spbstu.ru/article/2025.20.8/ 2025_1-5-6.pdf

RAR RUS 106-120 0000-0002-1399-1822 Peter the Great St. Petersburg Polytechnic University Shenets Nikolay shenets_nn@ibks.spbstu.ru 0000-0002-7485-4848 Peter the Great St. Petersburg Polytechnic University Aleksandrova Elena aleksandrova_eb@spbstu.ru Peter the Great St. Petersburg Polytechnic University Konoplev Artem konoplev_as@spbstu.ru 0000-0003-0623-9891 Peter the Great St. Petersburg Polytechnic University Gololobov Nikita gololobov_nv@spbstu.ru General solution to the special problem of distributing shares using Shamir’s secret sharing scheme In this paper, we solve the following problem. For a group of n participants, we need to distribute two shares to each of them in such a way that each pair of participants forms a (3, 4)-threshold access structure. In other words, each pair of participants can find some secret using any 3 out of the 4 shares they have. Obviously, this problem has a trivial solution: to share the same secret between everyone using a (3, 2n)-threshold secret sharing scheme. However, of theoretical and practical interest is the case when each pair of participants recovers a secret different from the others. In particular, the solution to this problem is necessary for the key agreement protocol proposed in [1]. In this paper, we find a complete solution to considered problem for Shamir’s secret sharing scheme. In addition, non-interactive methods for randomizing the key agreement protocol from [1] are studied. Unfortunately, it turns out that they do not enhance the security of this protocol. 10.48612/jisp/gh7t-814n-e9uz 004.056.53 (003.26) Key pre-distribution Shamir’s secret sharing scheme key agreement protocol perfectness threshold cryptography https://jisp.spbstu.ru/article/2025.20.9/ 2025_1-5-6.pdf

RAR RUS 121-131 0000-0001-9448-2386 Krasnodar Higher Military School named after General of the Army S. M. Shtemenko Novikov Pavel novikov.p.ark@yandex.ru 0000-0002-0644-4353 Krasnodar Higher Military School named after General of the Army S. M. Shtemenko Dichenko Sergei dichenko.sa@yandex.ru 0000-0001-9665-2174 Krasnodar Higher Military School named after General of the Army S. M. Shtemenko Lukyanov Roman 19sam@mail.ru 0000-0002-1122-3563 Krasnodar Higher Military School named after General of the Army S. M. Shtemenko Polikarenkov Sergei 0000-0002-6972-1768 Krasnodar Higher Military School named after General of the Army S. M. Shtemenko Martynov Mikhail chechin@mail.ru A mathematical model and methodology for evaluating the effectiveness of network monitoring of data transmission network security The article considers a network monitoring system for the security of a data transmission network operating under computer influences. One of the most urgent tasks in these conditions is the development of mechanisms for evaluating the effectiveness of network monitoring of data transmission network security from computer influences. A mathematical model and methodology are proposed, where the fundamental difference from the existing ones is a new approach to monitoring the security status of data transmission network elements from computer influences. 10.48612/jisp/pg74-3nxe-fa33 004.056.53 Data transmission network network security monitoring computer impacts efficiency assessment https://jisp.spbstu.ru/article/2025.20.10/ 2025_1-5-6.pdf

RAR RUS 132-144 0009-0006-6856-2108 Peter the Great St. Petersburg Polytechnic University Pahomov Maksim pahomov_ma@spbstu.ru Model of node interaction in a mobile ad-hoc network considering protection against active network attacks The features of the functioning of mobile self-organizing networks are considered. Models of node interaction in these networks are analyzed, taking into account protection against network attacks, and their advantages and disadvantages are highlighted. A model of node interaction in a mobile self-organizing network is proposed, considering protection against active network attacks based on early attack detection. Early detection of network attacks is achieved by predicting network parameters and further analyzing them using machine learning methods. A trust model is also used to exclude malicious nodes from the network. 10.48612/jisp/a3z4-17n4-4xvf 004.056 Information security ad-hoc networks model of node interaction intrusion detection systems https://jisp.spbstu.ru/article/2025.20.11/ 2025_1-5-6.pdf

RAR RUS 145-154 Peter the Great St. Petersburg Polytechnic University Skrypnikov Artem artem.skr456@gmail.com 0000-0002-2264-7513 Peter the Great St. Petersburg Polytechnic University Krundyshev Vasiliy krundyshev_vm@spbstu.ru 0000-0002-9732-0099 Peter the Great St. Petersburg Polytechnic University Kalinin Maxim max@ibks.spbstu.ru

Russia, 195251, St. Petersburg, Polytechnicheskaya str., 29

Anonymization of network traffic in blockchain systems by using garlic routing The task of protecting nodes of a blockchain system from security threats of user deanonymization, access restriction, and imposition of false data about the blockchain state is considered. A method of anonymizing the network traffic between nodes of a blockchain system based on garlic routing, supporting integration with consensus mechanism, has been proposed. As a result of experimental study, it is demonstrated that the presented method allows increasing the safety of blockchain systems applied in large-scale network infrastructures. 10.48612/jisp/nhfh-bxm9-hnh2 004.056 Blockchain deanonymization distributed ledger network traffic garlic routing https://jisp.spbstu.ru/article/2025.20.12/ 2025_1-5-6.pdf

RAR RUS 155-163 57200960264 0000-0001-6289-3295 Russian State Hydrometeorological University Sikarev Igor sikarev@yandex.ru

Russia, 192007, St. Petersburg, Voronezhskaya str., 79

0000-0003-0554-5790 Admiral Makarov State University of Maritime and Inland Shipping Abramov Valery val.abramov@mail.ru 0000-0002-5069-6144 Russian State Hydrometeorological University Prostakevich Konstantin atombyfreund@mail.ru Russian State Hydrometeorological University Abramova Alexandra alexandria567@mail.ru 0000-0001-7601-2874 Profinfotech LLC Chestnov Arsenii arsenij430@gmail.com Automation of archiving for atmospheric precipitation measurement information Considered issues of automation for measuring information archiving received from the OTT PARSIVEL2 laser disdrometer in form of messages with .dat format. It is shown that .dat format is not convenient for archiving in databases. As a result of performed research, methodology and toolkit was developed for automating the conversion of source messages for subsequent archiving in databases, taking into account the specifics of the SQL query language. 10.48612/jisp/r28m-trm5-pfu3 007.51 Automation archiving databases disdrometer autonomous surface vessels https://jisp.spbstu.ru/article/2025.20.13/ 2025_1-5-6.pdf