{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T20:01:45Z","timestamp":1783540905821,"version":"3.55.0"},"reference-count":71,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"JSC \u00abInstitute of Digital Engineering and Technology\u00bb, Almaty, Kazakhstan"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The escalating complexity of cyberattacks necessitates advanced strategies for their detection and mitigation. This study presents a hybrid model that integrates Graph Neural Networks (GNNs) with Long Short-Term Memory (LSTM) networks to reconstruct and predict attack vectors in cybersecurity. GNNs are employed to analyze the structural relationships within the MITRE ATT&amp;CK framework, while LSTM networks are utilized to model the temporal dynamics of attack sequences, effectively capturing the evolution of cyber threats. The combined approach harnesses the complementary strengths of these methods to deliver precise, interpretable, and adaptable solutions for addressing cybersecurity challenges. Experimental evaluation on the CICIDS2017 dataset reveals the model\u2019s strong performance, achieving an Area Under the Curve (AUC) of 0.99 on both balanced and imbalanced test sets, an F1-score of 0.85 for technique prediction, and a Mean Squared Error (MSE) of 0.05 for risk assessment. These findings underscore the model\u2019s capability to accurately reconstruct attack paths and forecast future techniques, offering a promising avenue for strengthening proactive defense mechanisms against evolving cyber threats.<\/jats:p>","DOI":"10.3390\/computers14080301","type":"journal-article","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T07:54:26Z","timestamp":1753343666000},"page":"301","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Hybrid Approach Using Graph Neural Networks and LSTM for Attack Vector Reconstruction"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6075-4870","authenticated-orcid":false,"given":"Yelizaveta","family":"Vitulyova","sequence":"first","affiliation":[{"name":"National Scientific Laboratory for the Collective Use of Information and Space Technologies (NSLC IST), Satbayev University, Satpaev Str., 22a, 050013 Almaty, Kazakhstan"},{"name":"JSC \u00abInstitute of Digital Engineering and Technology\u00bb, Satpaev Str., 22\/5, 050000 Almaty, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1184-9483","authenticated-orcid":false,"given":"Tetiana","family":"Babenko","sequence":"additional","affiliation":[{"name":"Department of Cybersecurity, International IT University, Manas Str., 34\/1, 050000 Almaty, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9160-5982","authenticated-orcid":false,"given":"Kateryna","family":"Kolesnikova","sequence":"additional","affiliation":[{"name":"Department of Information Systems, International IT University, Manas Str., 34\/1, 050000 Almaty, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7682-280X","authenticated-orcid":false,"given":"Nikolay","family":"Kiktev","sequence":"additional","affiliation":[{"name":"Department of Automation and Robotic Systems, National University of Life and Environmental Sciences of Ukraine, Heroiv Oborony Str., 15, 03041 Kyiv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0137-1252","authenticated-orcid":false,"given":"Olga","family":"Abramkina","sequence":"additional","affiliation":[{"name":"Department of Cybersecurity, International IT University, Manas Str., 34\/1, 050000 Almaty, Kazakhstan"},{"name":"Department of Cybersecurity, Almaty University of Power Engineering and Telecommunications Name After Gumarbek Daukeev, Baitursynuly Str., 126, 050013 Almaty, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,24]]},"reference":[{"key":"ref_1","unstructured":"IBM Security (2024). Cost of a Data Breach Report, Ponemon Institute. Available online: https:\/\/www.ibm.com\/reports\/data-breach."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"12","DOI":"10.63180\/jcsra.thestap.2025.2.2","article-title":"Analyzing cybersecurity risks and threats in IT infrastructure based on NIST framework","volume":"2025","author":"Aljumaiah","year":"2025","journal-title":"J. Cyber Secur. Risk Audit."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bilge, L., and Dumitras, T. (2012, January 16). Before We Knew It: An Empirical Study of Zero-Day Attacks in the Real World. Proceedings of the ACM CCS, Raleigh North, CI, USA.","DOI":"10.1145\/2382196.2382284"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1186\/s40537-024-00957-y","article-title":"Advancing cybersecurity: A comprehensive review of AI-driven detection techniques","volume":"11","author":"Salem","year":"2024","journal-title":"J. Big Data"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1145\/357830.357849","article-title":"The Base-Rate Fallacy and the Difficulty of Intrusion Detection","volume":"3","author":"Axelsson","year":"2000","journal-title":"ACM Trans. Inf. Syst. Secur. (TISSEC)"},{"key":"ref_6","unstructured":"Russell, S.J., and Norvig, P. (2021). Artificial Intelligence: A Modern Approach, Pearson. [4th ed.]."},{"key":"ref_7","unstructured":"Strom, B.E., Applebaum, A., Miller, D.P., Nickels, K.C., Pennington, A.G., and Thomas, C.B. (2018). MITRE ATT&CK: Design and philosophy. Technical Report, The MITRE Corporation. Available online: https:\/\/attack.mitre.org\/docs\/ATTACK_Design_and_Philosophy_March_2020.pdf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_9","unstructured":"Kipf, T.N., and Welling, M. (2017, January 24\u201326). Semi-Supervised Classification with Graph Convolutional Networks. Proceedings of the 5th International Conference on Learning Representations (ICLR 2017), Toulon, France. Available online: https:\/\/openreview.net\/forum?id=SJU4ayYgl."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Babenko, T., Toliupa, S., and Kovalova, Y. (2018, January 20\u201324). LVQ models of DDOS attacks identification. Proceedings of the 14th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET 2018), Lviv-Slavske, Ukraine.","DOI":"10.1109\/TCSET.2018.8336253"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Apruzzese, G., Colajanni, M., Ferretti, L., and Marchetti, M. (2019, January 28\u201331). Addressing Adversarial Attacks Against Security Systems Based on Machine Learning. Proceedings of the 11th International Conference on Cyber Conflict (CyCon), Tallinn, Estonia.","DOI":"10.23919\/CYCON.2019.8756865"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016, January 13). Why Should I Trust You? Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.","DOI":"10.1145\/2939672.2939778"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"14","DOI":"10.63180\/jcsra.thestap.2024.1.3","article-title":"Cyber Security in Data Breaches","volume":"2024","author":"Aldossary","year":"2024","journal-title":"J. Cyber Secur. Risk Audit."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5361","DOI":"10.1109\/TNSM.2024.3414267","article-title":"A Graph Learning-Based Approach for Lateral Movement Detection","volume":"21","author":"Rabbani","year":"2024","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3605775","article-title":"Deep Learning for Zero-day Malware Detection and Classification: A Survey","volume":"56","author":"Deldar","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_16","first-page":"Tyad23","article-title":"A systematic literature review on advanced persistent threat behaviors and its detection strategy","volume":"10","author":"Jamil","year":"2024","journal-title":"J. Cybersecur."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Enoch, S.Y., Ge, M., Hong, J.B., and Kim, D.S. (2021, January 24\u201327). Model-based Cybersecurity Analysis: Past Work and Future Directions. Proceedings of the Annual Reliability and Maintainability Symposium, Orlando, FL, USA.","DOI":"10.1109\/RAMS48097.2021.9605784"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Anjum, N., Latif, Z., Lee, C., Shoukat, I.A., and Iqbal, U. (2021). MIND: A Multi-Source Data Fusion Scheme for Intrusion Detection in Networks. Sensors, 21.","DOI":"10.3390\/s21144941"},{"key":"ref_19","first-page":"65","article-title":"Intrusion detection systems using long short-term memory (LSTM)","volume":"8","author":"Laghrissi","year":"2021","journal-title":"J. Big"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3127","DOI":"10.1109\/TNNLS.2019.2935975","article-title":"Unsupervised anomaly detection with LSTM neural networks","volume":"31","author":"Ergen","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2923","DOI":"10.1109\/COMST.2018.2844341","article-title":"Deep learning for IoT big data and streaming analytics: A survey","volume":"20","author":"Mohammadi","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_22","first-page":"448","article-title":"A comparative study on cyber threat intelligence: The security incident response perspective","volume":"24","author":"Schlette","year":"2021","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_23","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, Cornell University."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8890306","DOI":"10.1155\/2020\/8890306","article-title":"DL-IDS: Extracting features using CNN-LSTM hybrid network for intrusion detection system","volume":"2020","author":"Sun","year":"2020","journal-title":"Secur. Commun. Netw."},{"key":"ref_25","first-page":"136","article-title":"Applying long short-term memory recurrent neural networks to intrusion detection","volume":"56","author":"Staudemeyer","year":"2015","journal-title":"S. Afr. Comput. J."},{"key":"ref_26","first-page":"1024","article-title":"Inductive Representation Learning on Large Graphs","volume":"30","author":"Hamilton","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst. (NeurIPS)"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.future.2020.11.016","article-title":"Graph Convolutional Networks for Graphs Containing Missing Features","volume":"117","author":"Taguchi","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1145\/3236009","article-title":"A Survey of Methods for Explaining Black Box Models","volume":"51","author":"Guidotti","year":"2019","journal-title":"ACM Comput. Surv."},{"key":"ref_29","unstructured":"Mendes, C., and Rios, T.N. (2023). Explainable Artificial Intelligence and Cybersecurity: A Systematic Literature Review. arXiv."},{"key":"ref_30","first-page":"32","article-title":"GNNExplainer: Generating Explanations for Graph Neural Networks","volume":"2019","author":"Ying","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_31","first-page":"810","article-title":"New approaches to the development of information security systems for unmanned vehicles","volume":"31","author":"Ermukhambetova","year":"2023","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"ref_32","first-page":"206","article-title":"Environmental impact assessment procedure as the implementation of the value approach in environmental projects","volume":"2851","author":"Olekh","year":"2021","journal-title":"CEUR Workshop Proc."},{"key":"ref_33","first-page":"81","article-title":"Determining Key Risks for Modern distributed information systems","volume":"3018","author":"Palko","year":"2021","journal-title":"CEUR Workshop Proc."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Palko, D., Babenko, T., Bigdan, A., Kiktev, N., Hutsol, T., Kubo\u0144, M., Hnatiienko, H., Tabor, S., Gorbovy, O., and Borusiewicz, A. (2023). Cyber Security Risk Modeling in Distributed Information Systems. Appl. Sci., 13.","DOI":"10.3390\/app13042393"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/978-3-030-58124-4_27","article-title":"Detection of SQL Injection Attack Using Neural Networks","volume":"Volume 1265","author":"Shkarlet","year":"2021","journal-title":"Mathematical Modeling and Simulation of Systems (MODS\u20192020)"},{"key":"ref_36","first-page":"169","article-title":"Prioritizing Cybersecurity Measures with Decision Support Methods Using Incomplete Data","volume":"3241","author":"Hnatiienko","year":"2021","journal-title":"CEUR Workshop Proc."},{"key":"ref_37","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (May, January 30). Graph Attention Networks. Proceedings of the International Conference on Learning Representations (ICLR), Vancouver, BC, Canada."},{"key":"ref_38","unstructured":"Brody, S., Alon, U., and Yahav, E. (2022, January 25\u201329). How Attentive Are Graph Attention Networks?. Proceedings of the International Conference on Learning Representations (ICLR), Virtual Event."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: A Search Space Odyssey","volume":"28","author":"Greff","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_40","unstructured":"Rossi, E., Chamberlain, B., Frasca, F., Eynard, D., Monti, F., and Bronstein, M. (2020). Temporal Graph Networks for Deep Learning on Dynamic Graphs. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"478","DOI":"10.51594\/csitrj.v4i3.1500","article-title":"Real-Time Cybersecurity threat detection using machine learning and big data analytics: A comprehensive approach","volume":"4","author":"Onyewuchi","year":"2023","journal-title":"Comput. Sci. IT Res. J."},{"key":"ref_42","unstructured":"National Institute of Standards and Technology (NIST) (2018). Framework for Improving Critical Infrastructure Cybersecurity, Version 1.1; NIST Cybersecurity Framework."},{"key":"ref_43","first-page":"4765","article-title":"A Unified Approach to Interpreting Model Predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst. (NeurIPS)"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"71","DOI":"10.32628\/IJSRST2411427","article-title":"Enhancing Cyber Security: A Study of Data Preprocessing Techniques for Cyber Security Datasets","volume":"11","author":"Dhongade","year":"2024","journal-title":"Int. J. Sci. Res. Sci. Technol."},{"key":"ref_45","unstructured":"Jeong, Y.S., Huang, L.J.H., Yen, N.Y., and Shih, T.K. (2020, January 1\u20134). Efficient Data Noise-Reduction for Cyber Threat Intelligence System. Proceedings of the International Conference on Information Science and Applications (ICISA) 2020, Online."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/MIC.2023.3299435","article-title":"Knowledge-enhanced neurosymbolic artificial intelligence for cybersecurity and privacy","volume":"27","author":"Piplai","year":"2023","journal-title":"IEEE Internet Comput."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1049\/cmu2.12736","article-title":"Knowledge graph reasoning for cyber attack detection","volume":"18","author":"Gilliard","year":"2014","journal-title":"IET Commun."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Sayegh, H.R., Dong, W., and Al-madani, A.M. (2024). Enhanced intrusion detection with LSTM-based model, feature selection, and SMOTE for imbalanced data. Appl. Sci., 14.","DOI":"10.3390\/app14020479"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Amato, F., Cirillo, E., Fonisto, M., and Moccardi, A. (2024). Detecting adversarial attacks in IoT-enabled predictive maintenance with time-series data augmentation. Information, 15.","DOI":"10.3390\/info15110740"},{"key":"ref_50","first-page":"5577","article-title":"A novel stochastic model for cybersecurity metric inspired by Markov chain model and attack graphs","volume":"9","author":"Abdalla","year":"2020","journal-title":"Int. J. Sci. Technol. Res."},{"key":"ref_51","first-page":"52","article-title":"Leveraging GANs for synthetic data generation to improve intrusion detection systems","volume":"1","author":"Rahman","year":"2025","journal-title":"J. Future Artif. Intell. Technol."},{"key":"ref_52","unstructured":"Mar\u00edn, J. (2025, July 15). Evaluating Synthetic Tabular Data Generated to Augment Small Sample Datasets. [Conference Paper]. Available online: https:\/\/www.semanticscholar.org\/paper\/a6f9fa2270477df2e94557800cfa0a11c1cb8cf3."},{"key":"ref_53","first-page":"3","article-title":"Synthetic Network Traffic Data Generation and Classification of Advanced Persistent Threat Samples: A Case Study with GANs and XGBoost","volume":"Volume 14149","author":"Anande","year":"2023","journal-title":"Artificial Intelligence Applications and Innovations"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhang, F., Dai, R., and Ma, X. (2023, January 29\u201331). AttRSeq: Attack story reconstruction via sequence mining on causal graph. Proceedings of the 2023 Third International Conference on Communications and Networking on Power, Electronics and Computer Applications (ICPECA), Shenyang, China.","DOI":"10.1109\/ICPECA56706.2023.10075886"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Mayer, R., Hittmeir, M., and Ekelhart, A. (2020). Privacy-Preserving Anomaly Detection Using Synthetic Data. Database and Expert Systems Applications, Springer.","DOI":"10.1007\/978-3-030-49669-2_11"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Grady, J.C., Wen, S.X., Maccarone, L.T., and Bowman, S.T. (2024, January 28\u201330). Statistical Methods for Developing Cybersecurity Response Thresholds for Operational Technology Systems Using Historical Data. Proceedings of the 2024 International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), Washington, DC, USA.","DOI":"10.1109\/TPS-ISA62245.2024.00075"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"ElSalamony, F., Barakat, N.H., and Mostafa, A. (2025, January 6\u20138). Unravelling the Sequential Patterns of Cyber Attacks: A Temporal Analysis of Attack Dependencies. Proceedings of the International Conference on Internet of Things, Big Data and Security (IoTBDS 2025), Porto, Portugal.","DOI":"10.5220\/0013436500003944"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ekisa, C., \u00d3 Briain, D., and Kavanagh, Y. (2024, January 13\u201314). Leveraging the MITRE ATT&CK Framework for Threat Identification and Evaluation in Industrial Control System Simulations. Proceedings of the Irish Signals and Systems Conference (ISSC 2024), Belfast, UK.","DOI":"10.1109\/ISSC61953.2024.10602968"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhang, S., Xue, X., and Su, X. (2025). DeepOP: A Hybrid Framework for MITRE ATT&CK Sequence Prediction via Deep Learning and Ontology. Electronics, 14.","DOI":"10.3390\/electronics14020257"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Dion\u00edsio, N., Alves, F., Ferreira, P.M., and Bessani, A. (2020, January 19\u201324). Towards end-to-end Cyberthreat Detection from Twitter using Multi-Task Learning. Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), Glasgow, UK.","DOI":"10.1109\/IJCNN.2019.8852475"},{"key":"ref_61","first-page":"3548","article-title":"HyperAdam: A Learnable Task-Adaptive Adam for Network Training","volume":"33","author":"Wang","year":"2019","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_62","unstructured":"Tan, M.M., Zainudin, Z., Muslim, N., Jamil, N.S., Mat Jan, N.A., Ibrahim, N., and Sabri, N.A. (2024, January 3\u20134). Intrusion Detection System (IDS) Classifications Using Hyperparameter Tuning for Machine Learning and Deep Learning. Proceedings of the 2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS), Bangkok, Thailand."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Abdallah, M., Le-Khac, N.-A., Jahromi, H.Z., and Jurcut, A. (2021, January 17\u201320). A Hybrid CNN-LSTM Based Approach for Anomaly Detection Systems in SDNs. Proceedings of the 16th International Conference on Availability, Reliability and Security (ARES 2021), Vienna, Austria.","DOI":"10.1145\/3465481.3469190"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Premkumar, M., Lakshmi, R., Velrajkumar, P., Priya, S., Tanguturi, R., Murali, S., and Sivaramkrishnan, M. (2023, January 17\u201319). Hybrid Deep Learning Model for Cyber-Attack Detection. Proceedings of the 2023 International Conference on Intelligent Computing and Control Systems (ICICCS), Tamil Nadu, India.","DOI":"10.1109\/ICICCS56967.2023.10142571"},{"key":"ref_65","first-page":"125","article-title":"Federated LSTM Model for Enhanced Anomaly Detection in Cyber Security: A Novel Approach for Distributed Threat","volume":"15","author":"Sahu","year":"2024","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Corsini, A., Yang, S., and Apruzzese, G. (2021, January 17\u201320). On the Evaluation of Sequential Machine Learning for Network Intrusion Detection. Proceedings of the 16th International Conference on Availability, Reliability and Security (ARES 2021), Vienna, Austria.","DOI":"10.1145\/3465481.3470065"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Babenko, T., Kolesnikova, K., Panchenko, M., Abramkina, O., Kiktev, N., Meish, Y., and Mazurchuk, P. (2025). Risk Assessment of Cryptojacking Attacks on Endpoint Systems: Threats to Sustainable Digital Agriculture. Sustainability, 17.","DOI":"10.3390\/su17125426"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Kalivoshko, O., Myrvoda, A., Kraevsky, V., Paranytsia, N., Skoryk, O., and Kiktev, N. (2022, January 10\u201312). Accounting and Analytical Aspect of Reflection of Foreign Economic Security of Ukraine. Proceedings of the 2022 IEEE 9th International Conference on Problems of Infocommunications, Science and Technology (PIC S&T), Kharkiv, Ukraine.","DOI":"10.1109\/PICST57299.2022.10238523"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Kiktev, N., Rozorinov, H., and Masoud, M. (2017, January 20\u201323). Information model of traction ability analysis of underground conveyors drives. Proceedings of the 2017 XIIIth International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH), Lviv, Ukraine.","DOI":"10.1109\/MEMSTECH.2017.7937552"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Kraevsky, V., Kostenko, O., Kalivoshko, O., Kiktev, N., and Lyutyy, I. (2019, January 8\u201311). Financial Infrastructure of Telecommunication Space: Accounting Information Attributive of Syntalytical Submission. Proceedings of the 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T), Kyiv, Ukraine.","DOI":"10.1109\/PICST47496.2019.9061494"},{"key":"ref_71","first-page":"1160","article-title":"Modification of the Danzig-Wolf Decomposition Method for Building Hierarchical Intelligent Systems","volume":"15","author":"Velyamov","year":"2024","journal-title":"Int. J. Adv. Comput. Sci. Appl. 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