{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:42:57Z","timestamp":1760060577005,"version":"build-2065373602"},"reference-count":70,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>This article solves the anomalies\u2019 operational detection in the network traffic problem for cyber police units by developing an adaptive neural network platform combining a variational autoencoder with continuous stochastic dynamics of the latent space (integration according to the Euler\u2013Maruyama scheme), a continuous\u2013discrete Kalman filter for latent state estimation, and Hotelling\u2019s T2 statistical criterion for deviation detection. This paper implements an online learning mechanism (\u201con the fly\u201d) via the Euler Euclidean gradient step. Verification includes variational autoencoder training and validation, ROC\/PR and confusion matrix analysis, latent representation projections (PCA), and latency measurements during streaming processing. The model\u2019s stable convergence and anomalies\u2019 precise detection with the metrics precision is \u22480.83, recall is \u22480.83, the F1-score is \u22480.83, and the end-to-end delay of 1.5\u20136.5 ms under 100\u20131000 sessions\/s load was demonstrated experimentally. The computational estimate for typical model parameters is \u22485152 operations for a forward pass and \u224838,944 operations, taking into account batch updating. At the same time, the main bottleneck, the O(m3) term in the Kalman step, was identified. The obtained results\u2019 practical significance lies in the possibility of the developed adaptive neural network platform integrating into cyber police units (integration with Kafka, Spark, or Flink; exporting incidents to SIEM or SOAR; monitoring via Prometheus or Grafana) and in proposing applied optimisation paths for embedded and high-load systems.<\/jats:p>","DOI":"10.3390\/computation13090221","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T14:23:50Z","timestamp":1757600630000},"page":"221","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Neural Network System for Detecting Unauthorised Intrusions Based on Real-Time Traffic Analysis"],"prefix":"10.3390","volume":"13","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"}]},{"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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9676-0180","authenticated-orcid":false,"given":"Vasyl","family":"Lytvyn","sequence":"additional","affiliation":[{"name":"Information Systems and Networks Department, Lviv Polytechnic National University, 12, Bandera Street, 79013 Lviv, Ukraine"}]},{"given":"Anatolii","family":"Komziuk","sequence":"additional","affiliation":[{"name":"Department of Administrative Law and Procedure, Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine"}]},{"given":"Oleksandr","family":"Prokudin","sequence":"additional","affiliation":[{"name":"Department of Organization of Educational and Scientific Training, Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine"}]},{"given":"Andrii","family":"Ostapiuk","sequence":"additional","affiliation":[{"name":"Lviv State University of Life Safety, 35, Kleparivska Street, 79000 Lviv, Ukraine"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Diana, L., Dini, P., and Paolini, D. (2025). Overview on Intrusion Detection Systems for Computers Networking Security. Computers, 14.","DOI":"10.3390\/computers14030087"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dini, P., Elhanashi, A., Begni, A., Saponara, S., Zheng, Q., and Gasmi, K. (2023). Overview on Intrusion Detection Systems Design Exploiting Machine Learning for Networking Cybersecurity. Appl. 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