{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T04:52:20Z","timestamp":1778215940855,"version":"3.51.4"},"reference-count":80,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>This article develops a method for the early detection of low-intensity DDoS attacks based on a three-factor vector metric and implements an applied hybrid neural network traffic analysis system that combines preprocessing stages, competitive pretraining (SOM), a radial basis layer, and an associative Grossberg output, followed by gradient optimisation. The initial tools used are statistical online estimates (moving or EWMA estimates), CUSUM-like statistics for identifying small stable shifts, and deterministic signature filters. An algorithm has been developed that aggregates the components of fragmentation, reception intensity, and service availability into a single index. Key features include the physically interpretable features, a hybrid neural network architecture with associative stability and low computational complexity, and built-in mechanisms for adaptive threshold calibration and online training. An experimental evaluation of the developed method using real telemetry data demonstrated high recognition performance of the proposed approach (accuracy is 0.945, AUC is 0.965, F1 is 0.945, localisation accuracy is 0.895, with an average detection latency of 55 ms), with these results outperforming the compared CNN-LSTM and Transformer solutions. The scientific contribution of this study lies in the development of a robust, computationally efficient, and application-oriented solution for detecting low-intensity attacks with the ability to integrate into edge and SOC systems. Practical recommendations for reducing false positives and further improvements through low-training methods and hardware acceleration are also proposed.<\/jats:p>","DOI":"10.3390\/computers15020084","type":"journal-article","created":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T10:48:01Z","timestamp":1770115681000},"page":"84","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Neural Network Method for Detecting Low-Intensity DDoS Attacks with Stochastic Fragmentation and Its Adaptation to Law Enforcement Activities in the Cyber Protection of Critical Infrastructure Facilities"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8009-5254","authenticated-orcid":false,"given":"Serhii","family":"Vladov","sequence":"first","affiliation":[{"name":"Department of Scientific Activity Organisation, Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine"},{"name":"Department of Combating Cybercrime, Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6417-3689","authenticated-orcid":false,"given":"Victoria","family":"Vysotska","sequence":"additional","affiliation":[{"name":"Department of Combating Cybercrime, Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine"},{"name":"Information Systems and Networks Department, Lviv Polytechnic National University, 12, Bandera Street, 79013 Lviv, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7365-8888","authenticated-orcid":false,"given":"\u0141ukasz","family":"\u015acis\u0142o","sequence":"additional","affiliation":[{"name":"Department of Automation and Computer Engineering, Cracow University of Technology, 24, Warszawska, 31-155 Crak\u00f3w, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rafa\u0142","family":"Dymczyk","sequence":"additional","affiliation":[{"name":"Faculty of Polish and Classical Philology, Adam Mickiewicz University in Pozna\u0144, 1, Wieniawskiego St., 61-712 Pozna\u0144, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oleksandr","family":"Posashkov","sequence":"additional","affiliation":[{"name":"Laboratory of Engineering and Transport and Road Technical Research, National Scientific Centre \u00abHon. Prof. M. S. Bokarius Forensic Science Institute\u00bb, 8-A, Zolochivska Street, 61177 Kharkiv, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6528-9867","authenticated-orcid":false,"given":"Mariia","family":"Nazarkevych","sequence":"additional","affiliation":[{"name":"Department of Combating Cybercrime, Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oleksandr","family":"Yunin","sequence":"additional","affiliation":[{"name":"Vice-Rector, Dnipro State University of Internal Affairs, 26, Nauky Avenue, 49005 Dnipro, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liliia","family":"Bobrishova","sequence":"additional","affiliation":[{"name":"Education Quality Assurance Department, Dnipro State University of Internal Affairs, 26, Nauky Avenue, 49005 Dnipro, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yevheniia","family":"Pylypenko","sequence":"additional","affiliation":[{"name":"Research Laboratory on Current Issues of Criminal Analysis of the Educational and Scientific Institute for Training Specialists for Criminal Police Units of the National Police of Ukraine, Odesa State University of Internal Affairs, 1 Uspenska Street, 65014 Odesa, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wynn, M. (2025). New Information Communication Technologies in the Digital Era. Information, 16.","DOI":"10.3390\/info16100886"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Batool, S., Aslam, M., Akpokodje, E., and Jilani, S.F. (2025). A Comprehensive Review of DDoS Detection and Mitigation in SDN Environments: Machine Learning, Deep Learning, and Federated Learning Perspectives. Electronics, 14.","DOI":"10.3390\/electronics14214222"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"204628","DOI":"10.1109\/ACCESS.2025.3634478","article-title":"Neural Network DDoS Mitigation System with Forensic Audit Support for Cyber Police","volume":"13","author":"Vladov","year":"2025","journal-title":"IEEE Access"},{"key":"ref_4","unstructured":"(2025, November 08). DDoS Attack Analytics 2025: Who Is Under the Gun, When They Attack and How to Protect Yourself Source. Available online: https:\/\/hub.kyivstar.ua\/articles\/analitika-d-do-s-atak-2025-hto-pid-priczilom-koli-atakuyut-i-yak-zahistitisya."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"D\u00edaz-Verdejo, J., Mu\u00f1oz-Calle, J., Estepa Alonso, A., Estepa Alonso, R., and Madinabeitia, G. (2022). On the Detection Capabilities of Signature-Based Intrusion Detection Systems in the Context of Web Attacks. Appl. Sci., 12.","DOI":"10.3390\/app12020852"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1007\/978-3-031-04036-8_6","article-title":"Signature-Based Detection of Botnet DDoS Attacks","volume":"Volume 13300","author":"Szynkiewicz","year":"2022","journal-title":"Cybersecurity of Digital Service Chains: Challenges, Methodologies, and Tools; Lecture Notes in Computer Science"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.aej.2025.03.068","article-title":"Signature and Anomaly Based Intrusion Detection System for Secure IoTs and V2G Communication","volume":"125","author":"Alnasser","year":"2025","journal-title":"Alex. Eng. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1726","DOI":"10.1038\/s41598-025-85866-7","article-title":"Signature-Based Intrusion Detection Using Machine Learning and Deep Learning Approaches Empowered with Fuzzy Clustering","volume":"15","author":"Ahmed","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103117","DOI":"10.1016\/j.cose.2023.103117","article-title":"Detecting DDoS Attacks Using Adversarial Neural Network","volume":"127","author":"Mustapha","year":"2023","journal-title":"Comput. Secur."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103661","DOI":"10.1016\/j.cose.2023.103661","article-title":"DDoS Attack Detection and Mitigation Using Deep Neural Network in SDN Environment","volume":"138","author":"Hnamte","year":"2024","journal-title":"Comput. Secur."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3929","DOI":"10.1109\/TNET.2024.3408675","article-title":"Correlation-Aware Neural Networks for DDoS Attack Detection in IoT Systems","volume":"32","author":"Hekmati","year":"2024","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"25623","DOI":"10.1109\/ACCESS.2024.3367357","article-title":"A Genetic Algorithm- and t-Test-Based System for DDoS Attack Detection in IoT Networks","volume":"12","author":"Saiyed","year":"2024","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Saiyed, M.F., and Al-Anbagi, I. (2025, January 8\u201312). A Genetic Algorithm and Game-Theoretic Model for DDoS Defense in IoT Networks. Proceedings of the ICC 2025\u2014IEEE International Conference on Communications, Montreal, QC, Canada.","DOI":"10.1109\/ICC52391.2025.11161899"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Garg, M., R, S.K., Mishra, V.A., Kuppuraj, T., Parashar, K., and S, S. (2024, January 13\u201314). Leveraging Data Mining Classifiers for Robust Ddos Attack Detection in Cloud Computing. Proceedings of the 2024 IEEE 4th International Conference on ICT in Business Industry & Government (ICTBIG), Indore, India.","DOI":"10.1109\/ICTBIG64922.2024.10911541"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"13098","DOI":"10.1038\/s41598-024-84879-y","article-title":"Distributed Denial-of-Service (DDOS) Attack Detection Using Supervised Machine Learning Algorithms","volume":"15","author":"Abiramasundari","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sapkota, B., Ray, A., Yadav, M.K., Dawadi, B.R., and Joshi, S.R. (2025). Machine Learning-Based Attack Detection and Mitigation with Multi-Controller Placement Optimization over SDN Environment. J. Cybersecur. Priv., 5.","DOI":"10.3390\/jcp5010010"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"19063","DOI":"10.1038\/s41598-025-03868-x","article-title":"Distributed Denial of Service (DDoS) Classification Based on Random Forest Model with Backward Elimination Algorithm and Grid Search Algorithm","volume":"15","author":"Sawah","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"205","DOI":"10.35940\/ijitee.A8166.1110120","article-title":"Svm Implementation for Ddos Attacks in Software Defined Networks","volume":"10","author":"Midha","year":"2020","journal-title":"Int. J. Innov. Technol. Explor. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, L., Yu, W., Wu, Z., and Peng, S. (2025). XGBoost-Based Detection of DDoS Attacks in Named Data Networking. Future Internet, 17.","DOI":"10.3390\/fi17050206"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2185","DOI":"10.1007\/s12083-024-01690-2","article-title":"An Intelligent Behavioral-Based DDOS Attack Detection Method Using Adaptive Time Intervals","volume":"17","author":"Shamekhi","year":"2024","journal-title":"Peer-to-Peer Netw. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dayal, N., and Srivastava, S. (2017, January 4\u20138). Analyzing Behavior of DDoS Attacks to Identify DDoS Detection Features in SDN. Proceedings of the 2017 9th International Conference on Communication Systems and Networks (COMSNETS), Bengaluru, India.","DOI":"10.1109\/COMSNETS.2017.7945387"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xiang, B., Zheng, R., Zhang, K., Li, C., and Zheng, J. (2025). FFT-RDNet: A Time\u2013Frequency-Domain-Based Intrusion Detection Model for IoT Security. Sensors, 25.","DOI":"10.3390\/s25154584"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"103228","DOI":"10.1016\/j.mex.2025.103228","article-title":"Time-Frequency Analysis and Autoencoder Approach for Network Traffic Anomaly Detection","volume":"14","author":"Purohit","year":"2025","journal-title":"MethodsX"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"101873","DOI":"10.1016\/j.measen.2025.101873","article-title":"Entropy Based Earlier Detection and Mitigation of DDOS Attack Using Stochastic Method in SDN_IOT","volume":"39","author":"Varalakshmi","year":"2025","journal-title":"Meas. Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ujjan, R.M.A., Pervez, Z., Dahal, K., Khan, W.A., Khattak, A.M., and Hayat, B. (2021). Entropy Based Features Distribution for Anti-DDoS Model in SDN. Sustainability, 13.","DOI":"10.3390\/su13031522"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Aladaileh, M.A., Anbar, M., Hintaw, A.J., Hasbullah, I.H., Bahashwan, A.A., and Al-Sarawi, S. (2022). Renyi Joint Entropy-Based Dynamic Threshold Approach to Detect DDoS Attacks against SDN Controller with Various Traffic Rates. Appl. Sci., 12.","DOI":"10.3390\/app12126127"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Aladaileh, M.A., Anbar, M., Hintaw, A.J., Hasbullah, I.H., Bahashwan, A.A., Al-Amiedy, T.A., and Ibrahim, D.R. (2023). Effectiveness of an Entropy-Based Approach for Detecting Low- and High-Rate DDoS Attacks against the SDN Controller: Experimental Analysis. Appl. Sci., 13.","DOI":"10.3390\/app13020775"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhan, S., Tang, D., Man, J., Dai, R., and Wang, X. (2019). Low-Rate DoS Attacks Detection Based on MAF-ADM. Sensors, 20.","DOI":"10.3390\/s20010189"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"101037","DOI":"10.1016\/j.measen.2024.101037","article-title":"Enhancing DDoS Attack Detection with Hybrid Feature Selection and Ensemble-Based Classifier: A Promising Solution for Robust Cybersecurity","volume":"32","author":"Hossain","year":"2024","journal-title":"Meas. Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Alshdadi, A.A., Almazroi, A.A., Ayub, N., Lytras, M.D., Alsolami, E., and Alsubaei, F.S. (2024). Big Data-Driven Deep Learning Ensembler for DDoS Attack Detection. Future Internet, 16.","DOI":"10.3390\/fi16120458"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Muduli, D., Bhatta, S., Shookdeb, S., Adhikari, A., Sapkota, A., and Chaturvedi, P. (2024, January 24\u201328). Enhancing DDoS Attack Detection: A Hybrid SVM-Decision Tree Ensemble Approach. Proceedings of the 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kamand, India.","DOI":"10.1109\/ICCCNT61001.2024.10725813"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kaplan, F., and Babal\u0131k, A. (2025). A-WHO: Stagnation-Based Adaptive Metaheuristic for Cloud Task Scheduling Resilient to DDoS Attacks. Electronics, 14.","DOI":"10.3390\/electronics14214337"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yu, Y., Cheng, G., Chen, Z., and Ding, H. (2021, January 13\u201315). A DDoS Protection Method Based on Traffic Scheduling and Scrubbing in SDN. Proceedings of the 2021 17th International Conference on Mobility, Sensing and Networking (MSN), Exeter, UK.","DOI":"10.1109\/MSN53354.2021.00119"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.comcom.2024.04.001","article-title":"SDN-Based Detection and Mitigation of DDoS Attacks on Smart Homes","volume":"221","author":"Garba","year":"2024","journal-title":"Comput. Commun."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"101527","DOI":"10.1016\/j.iot.2025.101527","article-title":"DDoSViT: IoT DDoS Attack Detection for Fortifying Firmware Over-The-Air (OTA) Updates Using Vision Transformer","volume":"30","author":"Ali","year":"2025","journal-title":"Internet Things"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"256","DOI":"10.9734\/air\/2024\/v25i51159","article-title":"A Survey of AI Methods for Detection of DDoS Attacks on Networks","volume":"25","author":"Otiko","year":"2024","journal-title":"Adv. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"105521","DOI":"10.1016\/j.rineng.2025.105521","article-title":"Adaptive Distributed Honeypot Detection Network for Enhanced Cybersecurity against DoS and DDoS Attacks","volume":"26","author":"Gopalakrishnan","year":"2025","journal-title":"Results Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"109642","DOI":"10.1016\/j.comnet.2023.109642","article-title":"Collaborative Prediction and Detection of DDoS Attacks in Edge Computing: A Deep Learning-Based Approach with Distributed SDN","volume":"225","author":"Zhou","year":"2023","journal-title":"Comput. Netw."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Neto, E.C.P., Dadkhah, S., and Ghorbani, A.A. (2022, January 22\u201324). Collaborative DDoS Detection in Distributed Multi-Tenant IoT Using Federated Learning. Proceedings of the 2022 19th Annual International Conference on Privacy, Security & Trust (PST), Fredericton, NB, Canada.","DOI":"10.1109\/PST55820.2022.9851984"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.comcom.2021.04.013","article-title":"Detection of Collaborative Misbehaviour in Distributed Cyber-Attacks","volume":"174","author":"Thoma","year":"2021","journal-title":"Comput. Commun."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.comcom.2015.06.012","article-title":"Detecting DDoS Attacks against Data Center with Correlation Analysis","volume":"67","author":"Xiao","year":"2015","journal-title":"Comput. Commun."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"102756","DOI":"10.1016\/j.jnca.2020.102756","article-title":"Online DDoS Attack Detection Using Mahalanobis Distance and Kernel-Based Learning Algorithm","volume":"168","author":"Kemmerich","year":"2020","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Daneshgadeh, S., Kemmerich, T., Ahmed, T., and Baykal, N. (2018, January 1\u20133). A Hybrid Approach to Detect DDoS Attacks Using KOAD and the Mahalanobis Distance. Proceedings of the 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA), Cambridge, MA, USA.","DOI":"10.1109\/NCA.2018.8548334"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1186\/s40537-025-01146-1","article-title":"Towards a Minimum Universal Features Set for IoT DDoS Attack Detection","volume":"12","author":"Ebrahem","year":"2025","journal-title":"J. Big Data"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"109361","DOI":"10.1016\/j.comnet.2022.109361","article-title":"DOCUS-DDoS Detection in SDN Using Modified CUSUM with Flash Traffic Discrimination and Mitigation","volume":"217","author":"Shalini","year":"2022","journal-title":"Comput. Netw."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"103597","DOI":"10.1016\/j.cose.2023.103597","article-title":"FLAD: Adaptive Federated Learning for DDoS Attack Detection","volume":"137","author":"Siracusa","year":"2024","journal-title":"Comput. Secur."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"111769","DOI":"10.1016\/j.comnet.2025.111769","article-title":"Edge AI-Based Self-Learning Technique for Mitigating DDoS Attacks in WSN","volume":"273","author":"Hussain","year":"2025","journal-title":"Comput. Netw."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Albshaier, L., Almarri, S., and Albuali, A. (2025). Federated Learning for Cloud and Edge Security: A Systematic Review of Challenges and AI Opportunities. Electronics, 14.","DOI":"10.3390\/electronics14051019"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1109\/TII.2022.3156642","article-title":"Federated Semisupervised Learning for Attack Detection in Industrial Internet of Things","volume":"19","author":"Aouedi","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Vladov, S., Chyrun, L., Muzychuk, E., Vysotska, V., Lytvyn, V., Rekunenko, T., and Basko, A. (2025). Intelligent Method for Generating Criminal Community Influence Risk Parameters Using Neural Networks and Regional Economic Analysis. Algorithms, 18.","DOI":"10.3390\/a18080523"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Geche, F., Batyuk, A., Mulesa, O., and Voloshchuk, V. (2020, January 21\u201325). The Combined Time Series Forecasting Model. Proceedings of the 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine.","DOI":"10.1109\/DSMP47368.2020.9204311"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Vladov, S., Yakovliev, R., Hubachov, O., Rud, J., Drozdova, S., and Perekrest, A. (2023, January 27\u201330). Modified Discrete Neural Network PID Controller for Controlling the Helicopters Turboshaft Engines Free Turbine Speed. Proceedings of the 2023 IEEE 5th International Conference on Modern Electrical and Energy System (MEES), Kremenchuk, Ukraine.","DOI":"10.1109\/MEES61502.2023.10402433"},{"key":"ref_53","first-page":"160","article-title":"Modified Neural Network Fault-Tolerant Closed Onboard Helicopters Turboshaft Engines Automatic Control System","volume":"3387","author":"Vladov","year":"2023","journal-title":"CEUR Workshop Proc."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Vladov, S., Shmelov, Y., Yakovliev, R., Petchenko, M., and Drozdova, S. (2022, January 20\u201322). Neural Network Method for Helicopters Turboshaft Engines Working Process Parameters Identification at Flight Modes. Proceedings of the 2022 IEEE 4th International Conference on Modern Electrical and Energy System (MEES), Kremenchuk, Ukraine.","DOI":"10.1109\/MEES58014.2022.10005670"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Izonin, I., Tkachenko, R., Yendyk, P., Pliss, I., Bodyanskiy, Y., and Gregus, M. (2024). Enhanced Input-Doubling Method Leveraging Response Surface Linearization to Improve Classification Accuracy in Small Medical Data Processing. Computation, 12.","DOI":"10.3390\/computation12100203"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Bodyanskiy, Y., Kulishova, N., and Chala, O. (2018). The Extended Multidimensional Neo-Fuzzy System and Its Fast Learning in Pattern Recognition Tasks. Data, 3.","DOI":"10.3390\/data3040063"},{"key":"ref_57","first-page":"869","article-title":"Error compensation in an intelligent sensing instrumentation system","volume":"Volume 2","author":"Sachenko","year":"2001","journal-title":"IMTC 2001. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Cat. No.01CH 37188), Budapest, Hungary, 21\u201323 May 2001"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"11","DOI":"10.47839\/ijc.21.1.2512","article-title":"Deep Neural Network with Adaptive Parametric Rectified Linear Units and Its Fast Learning","volume":"21","author":"Bodyanskiy","year":"2022","journal-title":"Int. J. Comput."},{"key":"ref_59","first-page":"24","article-title":"Influence of the Number of Neighbours on the Clustering Metric by Oscillatory Chaotic Neural Network with Dipole Synaptic Connections","volume":"3664","author":"Lytvyn","year":"2024","journal-title":"CEUR Workshop Proc."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Vladov, S., Banasik, A., Sachenko, A., Kempa, W.M., Sokurenko, V., Muzychuk, O., Pikiewicz, P., Molga, A., and Vysotska, V. (2024). Intelligent Method of Identifying the Nonlinear Dynamic Model for Helicopter Turboshaft Engines. Sensors, 24.","DOI":"10.3390\/s24196488"},{"key":"ref_61","first-page":"1639","article-title":"Optimization of Helicopters Aircraft Engine Working Process Using Neural Networks Technologies","volume":"3171","author":"Vladov","year":"2022","journal-title":"CEUR Workshop Proc."},{"key":"ref_62","first-page":"79","article-title":"Clusterization of Vector and Matrix Data Arrays Using the Combined Evolutionary Method of Fish Schools","volume":"4","author":"Bodyanskiy","year":"2022","journal-title":"Syst. Res. Inf. Technol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"103588","DOI":"10.1016\/j.cose.2023.103588","article-title":"A Deep Learning Technique to Detect Distributed Denial of Service Attacks in Software-Defined Networks","volume":"137","author":"Gadallah","year":"2024","journal-title":"Comput. Secur."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Jiyad, Z.M., Maruf, A.A., Haque, M.M., Gupta, M.S., Ahad, A., and Aung, Z. (2024, January 18\u201320). DDoS Attack Classification Leveraging Data Balancing and Hyperparameter Tuning Approach Using Ensemble Machine Learning with XAI. Proceedings of the 2024 Third International Conference on Power, Control and Computing Technologies (ICPC2T), Raipur, India.","DOI":"10.1109\/ICPC2T60072.2024.10475035"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"103674","DOI":"10.1016\/j.adhoc.2024.103674","article-title":"A Privacy-Preserving Self-Supervised Learning-Based Intrusion Detection System for 5G-V2X Networks","volume":"166","author":"Hossain","year":"2025","journal-title":"Ad Hoc Netw."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Peppes, N., Daskalakis, E., Alexakis, T., and Adamopoulou, E. (2025). A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis. Appl. Sci., 15.","DOI":"10.20944\/preprints202506.2537.v1"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Hernandez-Suarez, A., Sanchez-Perez, G., Toscano-Medina, L.K., Perez-Meana, H., Olivares-Mercado, J., Portillo-Portillo, J., Benitez-Garcia, G., Sandoval Orozco, A.L., and Garc\u00eda Villalba, L.J. (2023). ReinforSec: An Automatic Generator of Synthetic Malware Samples and Denial-of-Service Attacks through Reinforcement Learning. Sensors, 23.","DOI":"10.3390\/s23031231"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Bagui, S.S., Khan, M.P., Valmyr, C., Bagui, S.C., and Mink, D. (2025). Model Retraining upon Concept Drift Detection in Network Traffic Big Data. Future Internet, 17.","DOI":"10.3390\/fi17080328"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"4013","DOI":"10.1007\/s10994-022-06177-w","article-title":"Adversarial Concept Drift Detection under Poisoning Attacks for Robust Data Stream Mining","volume":"112","author":"Korycki","year":"2022","journal-title":"Mach. Learn."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Vladov, S., Vysotska, V., Sokurenko, V., Muzychuk, O., Nazarkevych, M., and Lytvyn, V. (2024). Neural Network System for Predicting Anomalous Data in Applied Sensor Systems. Appl. Syst. Innov., 7.","DOI":"10.3390\/asi7050088"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Kim, H., Shaik Kadu, Z.B., and Han, K. (2025). SMART DShot: Secure Machine-Learning-Based Adaptive Real-Time Timing Correction. Appl. Sci., 15.","DOI":"10.3390\/app15158619"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Mouri Zadeh Khaki, A., and Choi, A. (2025). Optimizing Deep Learning Acceleration on FPGA for Real-Time and Resource-Efficient Image Classification. Appl. Sci., 15.","DOI":"10.3390\/app15010422"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Li, Z., Li, H., and Meng, L. (2023). Model Compression for Deep Neural Networks: A Survey. Computers, 12.","DOI":"10.3390\/computers12030060"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Ralbovsk\u00fd, A., Kotuliak, I., and Sobolev, D. (2025). Evaluating Deployment of Deep Learning Model for Early Cyberthreat Detection in On-Premise Scenario Using Machine Learning Operations Framework. Computers, 14.","DOI":"10.3390\/computers14120506"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Mikkilineni, R., and Kelly, W.P. (2025). From Static Prediction to Mindful Machines: A Paradigm Shift in Distributed AI Systems. Computers, 14.","DOI":"10.20944\/preprints202511.1554.v1"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Gleyzer, S., Nguyen, H., Ramakrishnan, D.P., and Reinhardt, E.A.F. (2025). Sinusoidal Approximation Theorem for Kolmogorov\u2013Arnold Networks. Mathematics, 13.","DOI":"10.3390\/math13193157"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Babni, A., Jamiai, I., and Rodrigues, J.A. (2025). Error Estimates and Generalized Trial Constructions for Solving ODEs Using Physics-Informed Neural Networks. Math. Comput. Appl., 30.","DOI":"10.3390\/mca30060127"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Vladov, S., Shmelov, Y., Yakovliev, R., Petchenko, M., and Drozdova, S. (2022, January 10\u201312). Helicopters Turboshaft Engines Parameters Identification at Flight Modes Using Neural Networks. Proceedings of the IEEE 17th International Conference on Computer Science and Information Technologies (CSIT), Lviv, Ukraine.","DOI":"10.1109\/CSIT56902.2022.10000444"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Vladov, S., Shmelov, Y., and Yakovliev, R. (2022, January 3\u20137). Modified Searchless Method for Identification of Helicopters Turboshaft Engines at Flight Modes Using Neural Networks. Proceedings of the 2022 IEEE 3rd KhPI Week on Advanced Technology (KhPIWeek), Kharkiv, Ukraine.","DOI":"10.1109\/KhPIWeek57572.2022.9916422"},{"key":"ref_80","first-page":"82","article-title":"Helicopters Turboshaft Engines Parameters Identification Using Neural Network Technologies Based on the Kalman Filter","volume":"1980","author":"Vladov","year":"2023","journal-title":"Commun. Comput. Inf. Sci."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/15\/2\/84\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T10:54:49Z","timestamp":1770116089000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/15\/2\/84"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,1]]},"references-count":80,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["computers15020084"],"URL":"https:\/\/doi.org\/10.3390\/computers15020084","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,1]]}}}