АҚПАРАТТЫҚ ЖҮЙЕЛЕРДЕГІ КИБЕРҚАУІПТЕРДІ АНЫҚТАУ ҮШІН ЖАСАНДЫ ИНТЕЛЛЕКТТІ ҚОЛДАНУ
Article Sidebar
Main Article Content
Аңдатпа
Мақалада ақпараттық жүйелердегі киберқауіптерді анықтауға жасанды интеллект әдістерін қолдану қарастырылады. Жіктеу алгоритмдерін, жүйке желілерін және мінез-құлықты талдауды қоса алғанда, машиналық және терең оқытуға негізделген заманауи тәсілдер талданады. Дәстүрлі және интеллектуалды интрузияны анықтау жүйелерінің тиімділігіне салыстырмалы баға беріледі. Федеративті оқыту мен түсіндіруге болатын ЖИ сияқты перспективалық бағыттар қарастырылады. Ақпараттық қауіпсіздіктің бар инфрақұрылымына ЖИ-шешімдерін интеграциялау бойынша ұсыныстар ұсынылды.
Article Details
##submission.citations##
Committee for Information Security of the Ministry of Digital Development, Innovations and Aerospace Industry of the Republic of Kazakhstan. Report on the State of Cybersecurity in the Republic of Kazakhstan for 2023. Astana: MDDIAI RK, 2023. Available at: https://www.gov.kz/memleket/entities/mdi
Dolgin, A. E., & Makhanova, M. S. Modern threats to information security and methods of their neutralization. Bulletin of Karaganda Technical University, 2022, No. 4, pp. 45–53.
Cybersecurity Concept of the Republic of Kazakhstan “Cyber Shield of Kazakhstan” for 2017–2022. Approved by the Resolution of the Government of the Republic of Kazakhstan dated June 30, 2017, No. 407. Astana, 2017.
Syzdykov, B. K., & Zhumabekova, A. T. Application of machine learning algorithms for the classification of network attacks. Proceedings of the National Academy of Sciences of the Republic of Kazakhstan. Physical and Mathematical Series, 2021, No. 3, pp. 112–120.
Abdrakhmanov, N. S., & Nurmaganbetov, E. A. Detection of anomalies in network traffic using deep learning methods. Bulletin of KazNTU, 2022, No. 2, pp. 78–86.
Tazhibaeva, S. L., & Ibraev, A. M. Convolutional and recurrent neural networks in cybersecurity tasks: a review. In: Proceedings of the International Conference “Infocommunications and Information Technologies”. Almaty, 2023, pp. 198–205.
Yuan, X. et al. DeepDefense: Identifying DDoS Attack via Deep Learning. In: Proceedings of IEEE International Conference on Data Mining (ICDM), 2017, pp. 1204–1209.
Kuznetsov, A. A., & Belov, V. M. User behavior analysis as a tool for countering insider threats. Issues of Cybersecurity, 2022, No. 1(47), pp. 32–41.
Ospanov, R. B., & Seitkali, D. N. Intelligent methods for detecting phishing resources based on transformer models. Proceedings of Karaganda Technical University, 2023, No. 1, pp. 55–62.
Zhaksybekov, K. A., & Mukanov, D. R. Machine learning methods for malware analysis: current state and prospects. Bulletin of Karaganda Technical University, 2023, No. 2, pp. 101–109.
Liu, Y. et al. Towards Robust Graph Neural Network against Label Noise. IEEE Transactions on Knowledge and Data Engineering, 2022, Vol. 35, pp. 7399–7413.
Sidorov, I. D., & Petrov, S. V. Integration of AI technologies into SIEM systems of corporate security. Information Technology Security, 2022, Vol. 29, No. 4, pp. 22–35.
Musaev, T. K., & Nurtaev, A. B. Federated learning as a tool for collective counteraction to cyber threats without violating data confidentiality. In: Proceedings of the IV International Scientific and Practical Conference “Digital Kazakhstan”. Astana, 2023, pp. 310–317.
Li, T. et al. Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Processing Magazine, 2020, Vol. 37, No. 3, pp. 50–60.
Akhmetov, B. S., & Lakhno, V. A. Explainable AI models in cyberattack detection: methodology and practical applications. Applied Informatics, 2023, Vol. 18, No. 1, pp. 14–26.
Isabekov, D. T., & Seitkali, D. N. Methods of balancing training datasets in intrusion detection systems. Bulletin of KazNU. Series: Mathematics, Mechanics, Informatics, 2022, No. 4, pp. 66–74.
Goodfellow, I. J. et al. Explaining and Harnessing Adversarial Examples. International Conference on Learning Representations (ICLR), 2015. arXiv:1412.6572.