{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T17:42:10Z","timestamp":1781545330831,"version":"3.54.5"},"reference-count":89,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:00:00Z","timestamp":1761091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Thise article examines the reliable online detection and IDS\/IPS intrusion prevention in dynamic corporate networks problems, where traditional signature-based methods fail to keep pace with new and evolving attacks, and streaming data is susceptible to drift and targeted \u201cpoisoning\u201d of the training dataset. As a solution, we propose a hybrid neural network system with adaptive online training, a formal minimax false-positive control framework, and a robustness mechanism set (a Huber model, pruned learning rate, DRO, a gradient-norm regularizer, and a prioritized replay). In practice, the system combines modal encoders for traffic, logs, and metrics; a temporal GNN for entity correlation; a variational module for uncertainty assessment; a differentiable symbolic unit for logical rules; an RL agent for incident prioritization; and an NLG module for explanations and the preparation of forensically relevant artifacts. In this case, the applied components are connected via a cognitive layer (cross-modal fusion memory), a Bayesian-neural network fuser, and a single multi-task loss function. The practical implementation includes the pipeline \u201cnovelty detection \u2192 active labelling \u2192 incremental supervised update\u201d and chain-of-custody mechanisms for evidential fitness. A significant improvement in quality has been experimentally demonstrated, since the developed system achieves an ROC AUC of 0.96, an F1-score of 0.95, and a significantly lower FPR compared to basic architectures (MLP, CNN, and LSTM). In applied validation tasks, detection rates of \u224892\u201394% and resistance to distribution drift are noted.<\/jats:p>","DOI":"10.3390\/bdcc9110267","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T02:03:48Z","timestamp":1761185028000},"page":"267","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Neural Network IDS\/IPS Intrusion Detection and Prevention System with Adaptive Online Training to Improve Corporate Network Cybersecurity, Evidence Recording, and Interaction with Law Enforcement Agencies"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8009-5254","authenticated-orcid":false,"given":"Serhii","family":"Vladov","sequence":"first","affiliation":[{"name":"Department of Scientific Activity Organization, 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":[{"vocabulary":"crossref","role":"author"}]},{"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":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9786-5398","authenticated-orcid":false,"given":"Svitlana","family":"Vashchenko","sequence":"additional","affiliation":[{"name":"Legal Disciplines Department, Sumy Branch of Kharkiv National University of Internal Affairs, 24, Miru Street, 40007 Sumy, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Serhii","family":"Bolvinov","sequence":"additional","affiliation":[{"name":"Department of Organization of Educational and Scientific Training (Doctoral and Postgraduate Studies), Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Serhii","family":"Glubochenko","sequence":"additional","affiliation":[{"name":"Vitovskyi District Court of the Mykolaiv Region, 77, Olshantsev Street, 54050 Mykolayiv, Ukraine"},{"name":"Department of Constitutional and Administrative Law and Process, Petro Mohyla Black Sea National University, 10, 68-Desantnykiv Street, 54003 Mykolayiv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrii","family":"Repchonok","sequence":"additional","affiliation":[{"name":"Department of Organization of Educational and Scientific Training (Doctoral and Postgraduate Studies), Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maksym","family":"Korniienko","sequence":"additional","affiliation":[{"name":"Odesa State University of Internal Affairs, 1 Uspenska Street, 65014 Odesa, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruslan","family":"Herasymchuk","sequence":"additional","affiliation":[{"name":"Department of Organization of Educational and Scientific Training (Doctoral and Postgraduate Studies), Kharkiv National University of Internal Affairs, 27, L. 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