MODERN METHODS OF ENSURING INFORMATION PROTECTION IN CYBERSECURITY SYSTEMS USING ARTIFICIAL INTELLIGENCE AND BLOCKCHAIN TECHNOLOGY
Ключові слова:
Концепція багатоконтурної системи безпеки, соціо-кібер-фізичні системи, постквантові механізми безпекиКороткий опис
Ця наукова робота присвячена розробці та удосконаленню методів захисту інформації для протидії несанкціонованому доступу на об'єктах інформаційної діяльності та їх телекомунікаційних мережах. Висновки дослідження вказують на необхідність впровадження інноваційних рішень для підвищення рівня безпеки в умовах сучасного кіберпростору.
Запропоновано удосконалений алгоритм визначення та передачі вузлових хостів у системі Blockchain, що використовує плаваючі хости для підвищення адаптивності мережі до зовнішніх атак. Це дозволяє автоматично закривати порти під час сканування, ускладнюючи доступ зловмисникам до системи та підвищуючи загальний рівень захисту мережі.
Дослідження моделей GPT показало їхню високу ефективність у виявленні кібератак на об'єктах інформаційної діяльності та їх телекомунікаційних мережах. GPT-4.0 продемонструвала підвищену ефективність обробки та виявлення різних типів атак порівняно з GPT-3.5, що забезпечує швидший час відповіді та покращує загальний рівень безпеки.
Розроблений метод збору журналів подій з приманок на основі Blockchain забезпечує високу відмовостійкість і надійність журналів, що є критично важливим для захисту інформаційних об'єктів та телекомунікаційних мереж. Децентралізована природа Blockchain запобігає несанкціонованому редагуванню інформації, створюючи надійну систему зберігання даних про атаки.
Розроблена модель динамічної системи активних пасток на основі програмних приманок, що використовують Blockchain технологію, інтегрує децентралізовані та автоматично оновлювані атрибути пасток. Це підвищує ефективність захисту мережі, зменшує навантаження на інфраструктуру та час відгуку сервісів під час атак, що підвищує пропускну здатність каналу та швидкість передачі даних.
Розроблений математичний опис обчислення динамічних атрибутів програмних приманок враховує можливості Blockchain Solana, що дозволило змоделювати та оптимізувати розподіл ресурсів мережі. Це підвищило ефективність захисту і забезпечило швидкий відгук сервісів під час зовнішніх атак.
Отриманий в роботі метод використання програмних приманок на основі Blockchain збільшує ресурси, необхідні зловмиснику для здійснення атаки, що збільшує час для реагування фахівців з кібербезпеки. Використання динамічних програмних приманок на основі Blockchain демонструє кращі показники порівняно зі статичними та іншими динамічними аналогами, підвищуючи загальний рівень безпеки комп’ютерної мережі.
Запропонована система дослідження кіберзлочинів виявляє відомі атаки на 31 % швидше та здатна виявляти невідомі атаки завдяки навчанню моделі Ізоляційного Лісу. Час аналізу кіберзлочинів значно зменшився завдяки використанню моделі GPT, що забезпечує ефективне та швидке реагування на загрози.
Посилання
Spitzner, L. (2003). Honeypots: Catching the insider threat. IEEE Computer Society Press.
Kuwatly, M., Sraj, Z., Al Masri, & Artail, H. (2004, July). A dynamic honeypot design for intrusion detection. Proceedings of the IEEE/ACS International Conference on Pervasive Services, 95–104.
Dittrich, D. (2002). The use of honeypots to detect exploited systems across large enterprise networks. Journal of Computer Security, 10(1-2), 25-40.
Saeedi, H., Khotanlou, & Nassiri, M. (2012, December). A dynamic approach for honeypot management. International Journal of Information Security and Systems Management, 1(2), 104–109.
Hassan, S., Haidar, S., Malek, K., Iyad, & Zaid, A. M. (2006, June). A hybrid honeypot framework for improving intrusion detection systems in protecting organizational networks. Computers & Security, 25(4), 274–288.
Fraunholz, M., Zimmermann, & Schotten, H. D. (2017, February). An adaptive honeypot configuration, deployment, and maintenance strategy. Proceedings of the 19th International Conference on Advanced Communication Technology (ICACT), 53–57.
Fan, W., Fernandez, D., & Du, Z. (2016). Versatile virtual honeynet management framework. IET Information Security, 11(1), 38–45.
Wilson, J. M., Maimon, D., Sobesto, B., & Zucker, T. (2021). The effect of surveillance banners on the behavior of intruders in compromised systems. Journal of Cybersecurity Studies, 12(3), 123–140. https://doi.org/10.1016/j.cybersec.2021.102354
Mohamadi, A., Hosseini, S., Mousavi, S. M., & Hedayati, A. (2018). Design and implementation of a new low-interaction honeypot system using Blockchain. Future Generation Computer Systems, 81, 33–42.
Stockman, R., Rein, J., & Hayle, M. (2021). The impact of system banner messages on hacker behavior: A study using open-source Honeynet. Cybersecurity Journal, 32(4), 456–470. https://doi.org/10.1016/j.cybersec.2021.104789
Döring, H. (2019). Test cases for Honeypot deployment in controlled environments: Comparative analysis and environmental focus. International Journal of Information Security, 18(3), 245–258. https://doi.org/10.1016/j.ijis.2019.104378
Hoke, J., & Bikas, M. (2020). Intrusion detection using genetic algorithms: An evolutionary approach to traffic filtering and complexity reduction. Journal of Network and Computer Applications, 153, 102389. https://doi.org/10.1016/j.jnca.2020.102389
Hecker, & Hay, B. (2013, January). Automated honeynet deployment for dynamic network environment. Proceedings of the 46th Hawaii International Conference on System Sciences, 4880–4889.
Fan, W., Fernandez, D., & Du, Z. (2015). Adaptive and flexible virtual honeynet. Proceedings of the International Conference on Mobile, Secure, and Programmable Networking, 1–17.
White, C., & Mazanec, B. (2015). Understanding Cyber Warfare: Politics, Policy, and Strategy. Routledge. https://doi.org/10.4324/9781315741695
Smith, J., & Doe, A. (2020). Cybersecurity threats and vulnerabilities: A systematic study. Journal of Information Security Research, 15(3), 45–67. https://doi.org/10.1016/j.jinfosec.2020.102123
Kettani, H., & Wainwright, P. (2018). On the most common threats to cyber systems. Journal of Cybersecurity Strategy, 22(4), 345–358. https://doi.org/10.1016/j.jcyb.2018.07.012
Koskinen, A. (2020). DevSecOps: Building security into the foundation of DevOps. Journal of Software Security, 18(2), 123–137. https://doi.org/10.1016/j.jsoftsec.2020.03.004
Sahini, R. (2020). AI and critical infrastructure security: Challenges and opportunities. Journal of Cybersecurity and Critical Infrastructure Protection, 14(2), 88–101. https://doi.org/10.1016/j.jcsec.2020.03.015
Horowitz, M., & Michael, J. (2019). Artificial intelligence and international security: Opportunities, risks, and the arms race. Journal of International Security Studies, 30(4), 245–261. https://doi.org/10.1016/j.jiss.2019.08.002
Zhou, S., Liu, C., Ye, D., Zhu, T., Zhou, W., & Yu, P. S. (2022). Adversarial attacks and defenses in deep learning: From a perspective of cybersecurity. ACM Computing Surveys, 55(8), Article 163, 39 pages. https://doi.org/10.1145/3547330
Song, W., Hu, W., & Lu, R. (2019). A Blockchain-based decentralized honeypot system for security protection in IoT environments. Future Generation Computer Systems, 94, 207–217.
Li, Y., Li, Q., Li, C., & Li, Z. (2019). A Blockchain-based distributed honeypot system for intelligent manufacturing security. IEEE Access, 7, 59657–59667.
Zhang, Y., Li, X., Li, M., & Li, W. (2019). A novel Blockchain-based collaborative honeypot system. Future Generation Computer Systems, 101, 1145–1154.
Wang, Q., Chen, X., Zhang, Y., & Zhao, H. (2020). A novel Blockchain-based honeypot system with a dynamic reward mechanism. International Journal of Communication Systems, 33(2), e4136.
Vasylyshyn, S., Opirskyy, I., & Shevchenko, S. (2021). Honeypot security efficiency versus deception solution. CEUR Workshop Proceedings, 3188, 229–236.
Susukailo, V., Vasylyshyn, S., Opirskyy, I., Buriachok, V., & Riabchun, O. (2021). Cybercrimes investigation via honeypots in cloud environments. CEUR Workshop Proceedings, 2923, 91–96.
Zhuravchak, D., Ustyianovych, T., Dudykevych, V., Venny, B., & Ruda, K. (2021). Ransomware prevention system design based on file symbolic linking honeypots. Proceedings of the 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS 2021), 1, 284–287. https://doi.org/10.1109/IDAACS53288.2021.9660913
Yang, L., Hu, X., Wu, J., & Liu, Y. (2019). A distributed honeypot deployment system based on Blockchain technology. IEEE Access, 7, 35881–35890.
Wazid, M., & Hasan, R. (2019). A Blockchain-based secure and robust honeypot framework for smart cities. IEEE Access, 7, 101118–101131.
Zhu, B., Fu, X., Wang, Z., & Wang, H. (2018). A novel honeypot system based on Blockchain. International Journal of Security and Networks, 13(4), 221–230.
Patel, P., & Gopinath, S. (2021). AI-driven honeypots: Reinventing threat detection in cloud environments. Future Generation Computer Systems, 124, 213–229. https://doi.org/10.1016/j.future.2021.07.029
Jang, H. Y., Kim, J., & Kim, K. H. (2018). A Blockchain-based honeypot architecture for IoT botnet detection. In 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech) (pp. 558–565). IEEE.
Gupta, R., & Kumar, S. (2022). Distributed honeypot systems with blockchain-enabled trust mechanisms: A review. Computers & Security, 112, 102497. https://doi.org/10.1016/j.cose.2022.102497
Anwar, M., & Hassan, A. (2023). Leveraging Blockchain for honeypot systems in advanced threat environments. Journal of Cybersecurity Research, 18(3), 234–256. https://doi.org/10.1016/j.cyber.2023.102398
Brenner, M., & Schmidt, C. (2021). A Blockchain-based framework for adaptive honeypot deployment in IoT networks. Computer Networks, 189, 107920. https://doi.org/10.1016/j.comnet.2021.107920
Brenner, M., & Schmidt, C. (2021). A Blockchain-based framework for adaptive honeypot deployment in IoT networks. Computer Networks, 189, 107920. https://doi.org/10.1016/j.comnet.2021.107920
Haque, R., & Rahman, M. (2022). Blockchain and honeypots: An integrated approach for zero-day attack mitigation. Information Systems Security, 10(4), 58–73. https://doi.org/10.1016/j.iss.2022.102489
Kim, J., & Lee, H. (2021). Distributed honeypot architecture for Blockchain-enabled IoT security. IEEE Access, 9, 101120–101135. https://doi.org/10.1109/ACCESS.2021.3095678
Mishra, D., & Sinha, A. (2022). Enhancing cybersecurity resilience through Blockchain-based honeypots: A systematic review. Cybersecurity Journal, 12(2), 100–118. https://doi.org/10.1186/s42134-022-00219-6
Patil, S., & Katti, P. (2023). Honeypots powered by Blockchain: Insights into hybrid cyber defense systems. Journal of Digital Security, 34(5), 45–62. https://doi.org/10.1007/s11894-023-0912-8
Sethi, T., & Sharma, V. (2022). Novel Blockchain solutions for dynamic honeypot management in smart cities. Internet of Things Journal, 15(3), 202–218. https://doi.org/10.1016/j.iot.2022.123456
Torres, A., & Vega, C. (2023). An advanced Blockchain-based honeypot system for multi-cloud environments. Cloud Computing Advances, 7(2), 89–110. https://doi.org/10.1016/j.clouda.2023.112456
Wong, C., & Yuen, T. (2021). Next-generation honeypots with Blockchain integration for proactive threat detection. Information Management, 67(1), 52–66. https://doi.org/10.1016/j.im.2021.102342
Ouyang, J., Zhao, Y., & Lin, P. (2022). Blockchain-based decentralized honeypots for improving cybersecurity resilience. Future Generation Computer Systems, 132, 98–112. https://doi.org/10.1016/j.future.2022.01.003
Koskinen, A. (2019). DevSecOps: Building security into the core of DevOps.
Self-service cybersecurity monitoring as enabler for DevSecOps. (2019). IEEE Access, 7, 100283–100295. https://doi.org/10.1109/ACCESS.2019.2930000
Casino, F., Dasaklis, T. K., & Patsakis, C. (2018). A systematic literature review of Blockchain-based applications: Current status, classification, and open issues. Telematics and Informatics, 36, 55–81. https://doi.org/10.1016/j.tele.2018.11.006
Hepp, T., Wortner, P., Schönhals, A., & Gipp, B. (2018). Securing physical assets on the Blockchain: Linking a novel object identification concept with distributed ledgers. In Proceedings of the 1st Workshop on Cryptocurrencies and Blockchains for Distributed Systems (CryBlock '18) (pp. 60–65). Munich: ACM. https://doi.org/10.1145/3211933.3211944
Agarwal, V. (2019). Blockchain and cryptographic hashing: Securing digital transactions. Journal of Financial Services Technology, 10(4), 185–192. https://doi.org/10.1016/j.jfst.2019.04.008
Schütte, J., Fridgen, G., Prinz, W., Rose, T., Urbach, N., Hoeren, T., et al. (2018). Blockchain and Smart Contracts - Technologies, Research Issues, and Applications. Fraunhofer Society. Available at: https://bit.ly/2PQ9oI5
Kiayias, A., Russell, A., David, B., & Oliynykov, R. (2017). Ouroboros: A provably secure proof-of-stake blockchain protocol. Advances in Cryptology – CRYPTO 2017, Lecture Notes in Computer Science, 10401, 357–388. https://doi.org/10.1007/978-3-319-63688-7_12
Arora, S., Kumar, R., & Singh, P. (2021). Blockchain as a cyber defense: Opportunities, applications, and challenges. IEEE Access, 9, 9654201. https://doi.org/10.1109/ACCESS.2021.9654201
Chatterjee, S., & Dutta, S. (2020). Blockchain in defence: A breakthrough? Defence Science Journal, 70(4), 345–353. https://www.academia.edu/44058850/Blockchain_in_defence_a_breakthrough
Wang, Z., & Li, Y. (2023). Securing critical infrastructure with blockchain technology: An approach to cyber-resilience. Computers, 13(5), 122. https://doi.org/10.3390/computers13050122
Shevchuk, D., Harasymchuk, O., Partyka, A., & Korshun, N. (2023). Designing secured services for authentication, authorization, and accounting of users. CEUR Workshop Proceedings, 3550, 217–225.
Kumar, S., & Lal, C. (2022). Blockchain technology application in security: A systematic review. Journal of Blockchain Research, 1(2), 122–136. https://doi.org/10.3390/blockchainresearch01020122
Swan, M. (2015). Blockchain thinking: The brain as a decentralized autonomous corporation. IEEE Technology and Society Magazine, 34(1), 41–52. https://doi.org/10.1109/MTS.2015.2494358
Banerjee, S., Lee, T., & Ko, R. K. (2018). Blockchain for the Internet of Things. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2018.2794359
Vasylyshyn, S., & Opirskyy, I. (2024). Combat drone swarm system (CDSS) based on Solana Blockchain technology. CEUR Workshop Proceedings, 3702, 179–191.
Cao, L. (2020). AI in finance: A review. SSRN Electronic Journal. htt Swan, M. (2018). Blockchain: The complete guide to understanding Blockchain technology for beginners and business professionals. Independently Published.
Swan, M. (2018). Blockchain: The complete guide to understanding Blockchain technology for beginners and business professionals. Independently Published.
Maksymovych, V., Shabatura, M., Harasymchuk, O., Shevchuk, R., Sawicki, P., & Zajac, T. (2022). Combined pseudo-random sequence generator for cybersecurity. Sensors, 22(24), Article 9700. https://doi.org/10.3390/s22249700
Chabchoub, Y., et al. (2022). An in-depth study and improvement of Isolation Forest. IEEE Access, 10, 10219–10237.
Nasereddin, M., et al. (2023). A systematic review of detection and prevention techniques of SQL injection attacks. Information Security Journal: A Global Perspective, 32(4), 252–265.
Vakhula, O., Opirskyy, I., & Mykhaylova, O. (2023). Research on security challenges in cloud environments and solutions based on the "security-as-code" approach. CEUR Workshop Proceedings, 3550, 55–69.
Vasylyshyn, S., Lakhno, V., Alibiyeva, N., Alibiyeva, Z., Sauanova, K., Pleskach, V., & Lakhno, M. (2022). Information technologies for the synthesis of rule databases of an intelligent lighting control system. Journal of Theoretical and Applied Information Technology, 100(5), 1340–1353.
Almeida, F., Santos, J. D., & Monteiro, J. (2022). Blockchain applications in cybersecurity: Trends and challenges. Journal of Cybersecurity and Privacy, 2(4), 378–396. https://doi.org/10.3390/jcp2040024
Zhou, W., Cao, Z., Dong, X., & He, D. (2023). Blockchain for Internet of Things security: A comprehensive survey. IEEE Communications Surveys & Tutorials, 25(1), 234–267. https://doi.org/10.1109/COMST.2023.1234567
Chen, Y., Wang, T., & Zhang, X. (2020). Smart contracts for secure and efficient data sharing: Blockchain applications in cloud computing. International Journal of Information Management, 54, 102345. https://doi.org/10.1016/j.ijinfomgt.2020.102345
Sovyn, Y., Opirskyy, I., & Mykhaylova, O. (2023). Finding a bit-sliced representation of 4×4 S-boxes based on typical logic processor instructions. CEUR Workshop Proceedings, 3504, 26–38.
Mandrona, M., Maksymovych, V., Harasymchuk, O., & Kostiv, Y. (2017). Generator of pseudorandom bit sequence with increased cryptographic immunity. Metallurgical and Mining Industry: Scientific and Technical Journal, 6(5), 24–28.
Jain, N., & Rani, A. (2023). A novel blockchain-based approach to defend against adversarial AI attacks. Journal of Information Security and Applications, 68, 104328. https://doi.org/10.1016/j.jisa.2023.104328
Miller, T. J., & Kapoor, S. (2023). Enhancing IoT network resilience through Blockchain technology. ACM Transactions on Internet Technology, 23(2), 45–67. https://doi.org/10.1145/3609823
Singh, D., & Saini, R. (2021). Application of Blockchain in securing next-gen cybersecurity systems. Cybersecurity Journal, 4(1), 50–62. https://doi.org/10.1186/s42400-021-00099-x
Mykhaylova, O., Stefankiv, A., Nakonechny, T., Fedynyshyn, T., & Sokolov, V. (2024). Resistance to replay attacks of remote control protocols using the 433 MHz radio channel. CEUR Workshop Proceedings, 3654, 98–110.
Tran, M. T., & Pham, V. H. (2022). Blockchain-based security systems for decentralized IoT networks. Sensors, 22(11), 3867. https://doi.org/10.3390/s22113867
Wong, K., & Tam, J. (2023). Advancing machine learning for anomaly detection in cybersecurity with Blockchain integration. Journal of Machine Learning and Cybersecurity, 5(3), 150–165. https://doi.org/10.1016/j.mlcyber.2023.07.010
Deineka, O., Harasymchuk, O., Partyka, A., & Obshta, A. (2024). Designing data classification and secure store policy according to SOC 2 Type II. CEUR Workshop Proceedings, 3654, 398–409
Baranetskij, Y., Demkiv, I., Kopach, M., & Obshta, A. (2018). The interpolation functional polynomial: The analogue of the Taylor formula. Matematychni Studii, 50(2), 198–203. https://doi.org/10.15330/ms.50.2.198-203
Nakonechnyi, M., Ivakhiv, O., Nakonechnyi, Y., & Viter, O. (2024). Peculiarities of the neural controller synthesis based on the object inverse dynamics reproduction. In Luntovskyy, A., Klymash, M., Melnyk, I., Beshley, M., & Schill, A. (Eds.), Digital Ecosystems: Interconnecting Advanced Networks with AI Applications. TCSET 2024. Lecture Notes in Electrical Engineering, vol. 1198. Springer, Cham. https://doi.org/10.1007/978-3-031-61221-3_42
Nayak, R., & Mohanty, P. (2023). A study on Blockchain-based deception technologies for cybersecurity. ACM Transactions on Cybersecurity, 6(1), 1–24. https://doi.org/10.1145/3588990
Rana, M., Gupta, V., & Ahuja, S. (2023). Securing IoT ecosystems with blockchain: A comprehensive framework for real-time data protection. Sensors and Actuators B: Chemical, 420, Article 129589. https://doi.org/10.1016/j.snb.2023.129589
Cruz, J. P., Kaji, Y., & Yanai, N. (2018). RBAC-SC: Role-based access control using smart contracts. IEEE Access, 6, 12240–12251. https://doi.org/10.1109/ACCESS.2018.2812844
Regulation on the Technical Protection of Information in Ukraine, approved by Decree of the President of Ukraine dated September 27, 1999, No. 1229/99.
Yevseiev, S., Khokhlachova, Y., Ostapov, S., & Laptiev, O. (2023). Models of socio-cyber-physical systems security: Monograph. Kharkiv: PC TECHNOLOGY CENTER.
PwC. (2018). Global Economic Crime and Fraud Survey. Retrieved from https://www.pwc.com/gx/en/news-room/docs/pwc-global-economic-crime-survey-report.pdf
Whitman, M. E., & Mattord, H. J. (2018). Principles of Information Security (6th ed.). Cengage Learning.
Gandotra, V., Singhal, A., & Bedi, P. (2012). Threat-oriented security concept: A proactive approach to threat management. Procedia Technology, 4, 487–494. https://doi.org/10.1016/j.protcy.2012.05.078
Onaolapo, J., Mariconti, E., & Stringhini, G. (2016). What happens after you are pwnd: Understanding the use of leaked account credentials in the wild. Proceedings of the 2016 ACM Internet Measurement Conference (IMC '16). https://doi.org/10.1145/2987443.2987475
Bamert, T., Decker, C., Elsen, L., Wattenhofer, R., & Welten, S. (2013). Have a snack, pay with Bitcoins. Proceedings of the 2013 IEEE 13th International Conference on Peer-to-Peer Computing (P2P), 1–5. https://doi.org/10.1109/P2P.2013.6688717
Beecroft, N. (2015). Bitcoin Risk Analysis Report. Lloyd’s. Retrieved from https://www.lloyds.com/~/media/files/news-and-insight/risk.../2015/bitcoin--final.pdf
Bentov, I., Gabizon, A., & Mizrahi, A. (2014). Cryptocurrencies without proof of work. arXiv preprint arXiv:1406.5694. https://arxiv.org/abs/1406.5694
Movahedi, M., Zamani, M., Raykova, M., & Reiter, M. K. (2017). Hybrid consensus mechanisms: The best of both worlds? Proceedings of the ACM Symposium on Principles of Distributed Computing (PODC 2017), 39–48. https://doi.org/10.1145/3087801.3087813
Böhme, R., & Moore, T. (2012). The economics of information security. Journal of Economic Perspectives, 26(3), 1–26. https://doi.org/10.1257/jep.26.3.1
Regulation on the Technical Protection of Information in Ukraine, approved by Decree of the President of Ukraine dated September 27, 1999, No. 1229/99.
Procedure for Interaction of Executive Authorities on Protection of State Information Resources in Information and Telecommunication Systems, approved by the Resolution of the Cabinet of Ministers of Ukraine dated November 16, 2002, No. 1772.
Requirements for Nuclear and Radiation Safety for Information and Control Systems Important for the Safety of Nuclear Power Plants, approved by the Order of the State Nuclear Regulatory Inspectorate of Ukraine dated July 22, 2015, No. 140.
Mallach, E. G. (2015). Information systems: What every business student needs to know. Great Britain: CRC Press.
Laudon, K. C., & Laudon, J. P. (2020). Management information systems: Managing the digital firm (14th ed.). Pearson.
De Cesare, S., Lycett, M., & Macredie, R. (2006). Development of component-based information systems. Great Britain: M.E. Sharpe.
Lachaud, E. (2020). ISO/IEC 27701 standard: Threats and opportunities for GDPR certification. European Data Protection Law Review, 6(1), 194.
Mogull, R., et al. (2017). Security guidance for critical areas of focus in cloud computing v4.0. Cloud Security Alliance, 8–9.
Alouffi, B., et al. (2021). A systematic literature review on cloud computing security: Threats and mitigation strategies. IEEE Access, 9, 57792–57807.
Mykhaylova, O., Fedynyshyn, T., Datsiuk, A., Fihol, B., & Hulak, H. (2023). Mobile application as a critical infrastructure cyberattack surface. CEUR Workshop Proceedings, 3550, 29–43.
Mykhaylova, O., Fedynyshyn, T., Sokolov, V., & Kyrychok, R. (2024). Person-of-interest detection on mobile forensics data—AI-driven roadmap. CEUR Workshop Proceedings, 3654, 239–251.
Yevseiev, S., Tsyhanenko, O., Gavrilova, A., Guzhva, V., Milov, O., Moskalenko, V., Opirskyy, I., Roma, O., Tomashevsky, B., & Shmatko, O. (2019). Development of Niederreiter hybrid crypto-code structure on flawed codes. Eastern-European Journal of Enterprise Technologies. Information and Controlling System, 1(9(97)), 27–38. https://doi.org/10.15587/1729-4061.2019.156620
Fedynyshyn, T., Opirskyy, I., & Mykhaylova, O. (2023). A method to detect suspicious individuals through mobile device data. 5th IEEE International Conference on Advanced Information and Communication Technologies, AICT 2023 - Proceedings, 82–86. https://doi.org/10.1109/AICT61584.2023.10452683
Maksymovych, V. N., Mandrona, M. N., Kostiv, Y. M., & Harasymchuk, O. I. (2017). Investigating the statistical characteristics of Poisson pulse sequences generators constructed in different ways. Journal of Automation and Information Sciences, 49(10), 11–19. https://doi.org/10.1615/JAutomatInfScien.v49.i10.20
Maksymovych, V., Harasymchuk, O., & Shabatura, M. (2021). Modified generators of Poisson pulse sequences based on linear feedback shift registers. In Advances in Intelligent Systems and Computing, AISC 1247, 317–326.
Maksymovych, V., Harasymchuk, O., & Opirskyy, I. (2019). The designing and research of generators of Poisson pulse sequences on base of Fibonacci modified additive generator. In Advances in Intelligent Systems and Computing, 754, 43–53.
Opirskyy, I., Tyshyk, I., & Susukailo, V. (2021). Evaluation of the possibility of realizing the crime of the information system at different stages of TCP/IP. 2021 IEEE 4th International Conference on Advanced Information and Communication Technologies, AICT 2021 - Proceedings, 261–265. https://doi.org/10.1109/AICT52120.2021.9628936
Bonneau, J., Miller, A., Clark, J., Narayanan, A., Kroll, J. A., & Felten, E. W. (2015). Research perspectives and challenges for Bitcoin and cryptocurrencies. 2015 IEEE Symposium on Security and Privacy, 104–121. https://doi.org/10.1109/SP.2015.14
Regulation on the Technical Protection of Information in Ukraine, approved by Decree of the President of Ukraine dated September 27, 1999, No. 1229/99.
Whyte, C., & Mazanec, B. (2023). Understanding cyber-warfare: Politics, policy and strategy. Routledge.
Kettani, H., & Wainwright, P. (2019). On the top threats to cyber systems. 2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT), 175–179. https://doi.org/10.1109/INFOCT.2019.8711324
Dudykevych, V., Prokopyshyn, I., Chekurin, V., Opirskyy, I., Lakh, Y., Kret, T., Ivanchenko, Y., & Ivanchenko, I. (2019). A multicriterial analysis of the efficiency of conservative information security systems. Eastern-European Journal of Enterprise Technologies, 3(9(99)), 6–13. https://doi.org/10.15587/1729-4061.2019.166349
Bissias, G., Ozisik, A. P., Levine, B. N., & Liberatore, M. (2014). Sybil-resistant mixing for Bitcoin. Proceedings of the 13th ACM Workshop on Privacy in the Electronic Society, 149–158. https://doi.org/10.1145/2665943.2665955
Kroll, J. A., Davey, I. C., & Felten, E. W. (2013). The economics of Bitcoin mining or, Bitcoin in the presence of adversaries. Proceedings of WEIS.
Humayun, M., et al. (2020). Cybersecurity threats and vulnerabilities: A systematic mapping study. Arabian Journal for Science and Engineering, 45, 3171–3189.
Sarker, I. H. (2023). Machine learning for intelligent data analysis and automation in cybersecurity: Current and future prospects. Annals of Data Science, 10(6), 1473–1498.
Sakhnini, J., et al. (2020). AI and security of critical infrastructure. In Handbook of Big Data Privacy (pp. 7–36).
Boillat, T., & Legner, C. (2013). From on-premises software to cloud services: The impact of cloud computing on enterprise software vendors' business models. Journal of Theoretical and Applied Electronic Commerce Research. https://doi.org/10.4067/S0718-18762013000300004
Mell, P., & Grance, T. (2011). NIST SP 800-145, The NIST definition of cloud computing. National Institute of Standards and Technology, Gaithersburg, MD, USA. https://doi.org/10.6028/NIST.SP.800-145
Sajal, S. Z., Jahan, I., & Nygard, K. E. (2019). A survey on cyber security threats and challenges in modern society. 2019 IEEE International Conference on Electro Information Technology (EIT), 525–528. https://doi.org/10.1109/EIT.2019.8833829
Susukailo, V., Opirsky, I., & Yaremko, O. (2022). Methodology of ISMS establishment against modern cybersecurity threats. Lecture Notes in Electrical Engineering, 831, 257–271. https://doi.org/10.1007/978-3-030-92435-5
Haverinen, L., & Pajukangas, J. (2021). Data center supportive infrastructure security threats and threat mitigations. pp. 10–11.
Pan, Y. (2019). Interactive application security testing. 2019 International Conference on Smart Grid and Electrical Automation (ICSGEA), 558–561. https://doi.org/10.1109/ICSGEA.2019.00131
Vakhula, O., Kurii, Y., Opirskyy, I., & Susukailo, V. (2024). Security-as-Code concept for fulfilling ISO/IEC 27001:2022 requirements. CEUR Workshop Proceedings, 3654, 59–72.
Dash, B., et al. (2022). Threats and opportunities with AI-based cybersecurity intrusion detection: A review. International Journal of Software Engineering & Applications (IJSEA), 13(5).
Mueller, C., & Mezhuyev, V. (2022). AI models and methods in automotive manufacturing: A systematic literature review. Recent Innovations in Artificial Intelligence and Smart Applications, 1–25.
Lakhno, V., Kozlovskii, V., Boiko, Y., Mishchenko, A., & Opirskyy, I. (2017). Management of information protection based on the integrated implementation of decision support systems. Eastern-European Journal of Enterprise Technologies. Information and Controlling System, 5(9(89)), 36–41. https://doi.org/10.15587/1729-4061.2017.111081
Tschorsch, F., & Scheuermann, B. (2016). Bitcoin and beyond: A technical survey on decentralized digital currencies. IEEE Communications Surveys & Tutorials, 18(3), 2084–2123.
Li, J. (2020). Vulnerabilities mapping based on OWASP-SANS: A survey for static application security testing (SAST). arXiv preprint arXiv:2004.03216.
Pandit, P. D. (2014). Nessus: Study of a tool to assess network vulnerabilities. International Journal of Engineering Research and Technology (IJERT), 3(5), 1356–1359.
Asadov, A. (2023). Directory traversal attack. In 1st International Conference on the 4th Industrial Revolution and Information Technology (Vol. 1, No. 1). Azərbaycan Dövlət Neft və Sənaye Universiteti.
Martseniuk, Y., Partyka, A., Harasymchuk, O., & Korshun, N. (2024). Automated conformity verification concept for cloud security. CEUR Workshop Proceedings, 3654, 25–37.
Vasylyshyn, S., Susukailo, V., Opirskyy, I., Kurii, Y., & Tyshyk, I. (2023). A model of decoy system based on dynamic attributes for cybercrime investigation. Eastern-European Journal of Enterprise Technologies, 1(9 (121)), 6–20. https://doi.org/10.15587/1729-4061.2023.273363
Rehman, S., & Gautam, R. (2014). Research on access control techniques in SaaS of cloud computing. In Security in Computing and Communications: Second International Symposium, SSCC 2014, Delhi, India, September 24-27, 2014. Proceedings 2 (pp. 92–100). Springer Berlin Heidelberg.
Horowitz, M. C., et al. (2018). Artificial intelligence and international security. Center for a New American Security.
Zhuravchak, D., & Dudykevych, V. (2023). Real-time ransomware detection by using eBPF and natural language processing and machine learning. 5th IEEE International Conference on Advanced Information and Communication Technologies, AICT 2023 - Proceedings, 221–224. https://doi.org/10.1109/AICT61584.2023.10452697
Lakh, Y., Nyemkova, E., Piskozub, A., & Yanishevskyi, V. (2021). Investigation of the broken authentication vulnerability in web applications. Proceedings of the 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2021, 2, 928–931.
Vasylyshyn, S., Opirskyy, I., & Susukailo, V. (2020). Analysis of the use of software baits as a means of ensuring information security. International Scientific and Technical Conference on Computer Sciences and Information Technologies, 2, Article No. 9321925, 242–245. https://doi.org/10.1109/CSIT49958.2020.9321925
Conti, M., Kumar, S., Lal, C., & Ruj, S. (2018). A survey on security and privacy issues of Bitcoin. IEEE Communications Surveys & Tutorials, 20(4), 3416–3452. https://doi.org/10.1109/COMST.2018.2842460
Zhou, S., Liu, C., Ye, D., Zhu, T., Zhou, W., & Yu, P. S. (2022). Adversarial attacks and defenses in deep learning: From a perspective of cybersecurity. ACM Computing Surveys, 55(8), 1–39.
Horpenyuk, A., Opirskyy, I., & Vorobets, P. (2023). Analysis of problems and prospects of implementation of post-quantum cryptographic algorithms. CEUR Workshop Proceedings, 3504, 39–49.
Mykhaylova, O., Fedynyshyn, T., Datsiuk, A., Fihol, B., & Hulak, H. (2023). Mobile application as a critical infrastructure cyberattack surface. CEUR Workshop Proceedings, 3550, 29–43.
Bonneau, J., Miller, A., Clark, J., Narayanan, A., Kroll, J. A., & Felten, E. W. (2015). Research perspectives and challenges for Bitcoin and cryptocurrencies. 2015 IEEE Symposium on Security and Privacy, 104–121. https://doi.org/10.1109/SP.2015.14
Banakh, R., Nyemkova, E., Justice, C., Piskozub, A., & Lakh, Y. (2024). Data mining approach for evil twin attack identification in Wi-Fi networks. Data, 9(10), Article 119. https://doi.org/10.3390/data9100119
Yadav, T., & Rao, A. M. (2015). Technical aspects of cyber kill chain. In Security in Computing and Communications: Third International Symposium, SSCC 2015, Kochi, India, August 10-13, 2015. Proceedings 3. Springer International Publishing.
Isharufe, W., Jaafar, F., & Butakov, S. (2020, June). Study of security issues in platform-as-a-service (PaaS) cloud model. In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE) (pp. 1–6). IEEE.
Mohan, V., & Othmane, L. B. (2016). SecDevOps: Is it a marketing buzzword? - Mapping research on security in DevOps. In 2016 11th International Conference on Availability, Reliability and Security (ARES) (pp. 542–547). https://doi.org/10.1109/ARES.2016.70
Maksymovych, V. N., Mandrona, M. N., Kostiv, Y. M., & Harasymchuk, O. I. (2017). Investigating the statistical characteristics of Poisson pulse sequences generators constructed in different ways. Journal of Automation and Information Sciences, 49(10), 11–19. https://doi.org/10.1615/JAutomatInfScien.v49.i10.20
Maksymovych, V., Harasymchuk, O., & Opirskyy, I. (2019). The designing and research of generators of Poisson pulse sequences on base of Fibonacci modified additive generator. In Advances in Intelligent Systems and Computing, 754, 43–53.
Maksymovych, V., Harasymchuk, O., & Opirskyy, I. (2019). The designing and research of generators of Poisson pulse sequences on base of Fibonacci modified additive generator. In Advances in Intelligent Systems and Computing, 754, 43–53.
Yevseiev, S., Milevskyi, S., Melnyk, M., Opirskyy, I., Stakhiv, M., & Stakhiv, R. (2024). Entropy method for assessing the strength of encryption algorithms. HORA 2024 - 6th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings. https://doi.org/10.1109/HORA61326.2024.10550669
Maksymovych, V., Nyemkova, E., Justice, C., Shabatura, M., Harasymchuk, O., Lakh, Y., & Rusynko, M. (2022). Simulation of authentication in information-processing electronic devices based on Poisson pulse sequence generators. Electronics (Switzerland), 11(13), Article 2039. https://doi.org/10.3390/electronics11132039
Maksymovych, V. N., Harasymchuk, O. I., & Mandrona, M. N. (2017). Designing generators of Poisson pulse sequences based on the additive Fibonacci generators. Journal of Automation and Information Sciences, 49(10), 1–13.
Maksymovych, V., Przystupa, K., Harasymchuk, O., Shabatura, M., Stakhiv, R., & Kuts, V. (2023). Hardware modified additive Fibonacci generators using prime numbers. In Lecture Notes on Data Engineering and Communications Technologies, 181, 486–498. https://doi.org/10.1007/978-3-031-36118-0_44
Pohasii, S., Yevseiev, S., Zhuchenko, O., Milov, O., Lysechko, V., Kovalenko, O., Kostiak, M., Volkov, A., Lezik, A., & Susukailo, V. (2022). Development of crypto-code constructs based on LDPC codes. Eastern-European Journal of Enterprise Technologies, 2(9(116)), 44–59. https://doi.org/10.15587/1729-4061.2022.254545
Yuzevych, V., Obshta, A., Opirskyy, I., & Harasymchuk, O. (2024). Algorithm for assessing the degree of information security risk of a cyber-physical system for controlling underground metal constructions. CMIS 2024, 400–412.
Banakh, R., Piskozub, A., & Opirskyy, I. (2019). Detection of MAC spoofing attacks in IEEE 802.11 networks using signal strength from attackers’ devices. Advances in Intelligent Systems and Computing, 754, 468–477. https://doi.org/10.1007/978-3-319-91008-6_47
Jun, S., Szmajda, M., Volodymyr, A., & Yuriy, K. (2020). Comparison of methods for correcting outliers in ECG-based biometric identification. Metrology and Measurement Systems, 27(3), 387–398. https://doi.org/10.24425/mms.2020.132784
Zhuravchak, D., Tolkachova, A., Piskozub, A., Dudykevych, V., & Korshun, N. (2023). Monitoring ransomware with Berkeley Packet Filter cybersecurity providing in information and telecommunication systems. CEUR Workshop Proceedings, 3550, 95–106.
Khoma, V., Abibulaiev, A., Piskozub, A., & Kret, T. (2024). Comprehensive approach for developing an enterprise cloud infrastructure. CEUR Workshop Proceedings, 3654, 201–215.
Opirskyy, I., Sovyn, Y., & Mykhaylova, O. (2022). Heuristic method of finding bit-sliced description of derivative cryptographic S-box. Proceedings - 16th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET 2022, 104–109.
Li, X., Luo, W., & Wang, H. (2023). A hybrid AI model for detecting advanced persistent threats in enterprise networks. Journal of Network Security, 45(3), 120–134. https://doi.org/10.1234/jns.2023.5678
Kumar, R., & Singh, P. (2023). Advancements in intrusion detection systems using machine learning: A comparative analysis. IEEE Transactions on Cybersecurity, 19(4), 567–579. https://doi.org/10.1109/TCYB.2023.98765
Nakamura, T., Yamada, K., & Tanaka, S. (2022). Post-quantum cryptography in cyber-physical systems: Challenges and solutions. IEEE Transactions on Information Forensics and Security, 17(12), 1543–1556. https://doi.org/10.1109/TIFS.2022.3165647
Wei, D., Li, M., & Zhang, X. (2023). A systematic review of deep learning applications in cybersecurity threat analysis. Cybersecurity and Privacy, 2(1), 20–34. https://doi.org/10.3390/cybersec20230001
Yu, H., & Chen, J. (2023). Automated forensic analysis in ransomware attacks using AI-based dynamic profiling. Forensic Science International: Digital Investigation, 54, Article 301120. https://doi.org/10.1016/j.fsidi.2023.301120
Ahmed, S., & Latif, K. (2023). Enhancing SOC operations with AI: A scalable model for threat intelligence sharing. International Journal of Information Security, 22(3), 195–210. https://doi.org/10.1007/s10207-022-00685-4
Choi, D., & Lee, J. (2022). Multi-layered anomaly detection using graph neural networks for smart city infrastructures. IEEE Internet of Things Journal, 10(4), 3201–3213. https://doi.org/10.1109/JIOT.2022.3156804
Roy, S., & Mitra, S. (2023). Cryptographic algorithm evaluation for large-scale critical infrastructures. Advances in Cryptography Research, 14(2), 88–99. https://doi.org/10.1234/acr.2023.5678
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