{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T17:41:47Z","timestamp":1775324507010,"version":"3.50.1"},"reference-count":77,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T00:00:00Z","timestamp":1761782400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>The article presents a method for detecting low-intensity DDoS attacks, focused on identifying difficult-to-detect \u201clow-and-slow\u201d scenarios that remain undetectable by traditional defence systems. The key feature of the developed method is the statistical criteria\u2019s (\u03c72 and T statistics, energy ratio, reconstruction errors) integration with a combined neural network architecture, including convolutional and transformer blocks coupled with an autoencoder and a calibrated regressor. The developed neural network architecture combines mathematical validity and high sensitivity to weak anomalies with the ability to generate interpretable artefacts that are suitable for subsequent forensic analysis. The developed method implements a multi-layered process, according to which the first level statistically evaluates the flow intensity and interpacket intervals, and the second level processes features using a neural network module, generating an integral blend-score S metric. ROC-AUC and PR-AUC metrics, learning curve analysis, and the estimate of the calibration error (ECE) were used for validation. Experimental results demonstrated the superiority of the proposed method over existing approaches, as the achieved values of ROC-AUC and PR-AUC were 0.80 and 0.866, respectively, with an ECE level of 0.04, indicating a high accuracy of attack detection. The study\u2019s contribution lies in a method combining statistical and neural network analysis development, as well as in ensuring the evidentiary value of the results through the generation of structured incident reports (PCAP slices, time windows, cryptographic hashes). The obtained results expand the toolkit for cyber-attack analysis and open up prospects for the methods\u2019 practical application in monitoring systems and law enforcement agencies.<\/jats:p>","DOI":"10.3390\/data10110173","type":"journal-article","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T02:48:28Z","timestamp":1761878908000},"page":"173","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Method for Detecting Low-Intensity DDoS Attacks Based on a Combined Neural Network and Its Application in Law Enforcement Activities"],"prefix":"10.3390","volume":"10","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\/0000-0002-6117-5846","authenticated-orcid":false,"given":"Oksana","family":"Mulesa","sequence":"additional","affiliation":[{"name":"Department of Physics, Mathematics and Technologies, University of Pre\u0161ov, 3, N\u00e1mestie Legion\u00e1rov, 080 01 Pre\u0161ov, Slovakia"},{"name":"Department of Software Systems, Uzhhorod National University, 3, Narodna Square, 88000 Uzhhorod, 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\/0000-0002-3972-0115","authenticated-orcid":false,"given":"Petro","family":"Horvat","sequence":"additional","affiliation":[{"name":"Department of Computer Systems and Networks, Uzhhorod National University, 3, Narodna Square, 88000 Uzhhorod, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nataliia","family":"Paziura","sequence":"additional","affiliation":[{"name":"Aviation English Department, State University \u201cKyiv Aviation Institute\u201d, 1, Liubomyra Huzara Avenue, 03680 Kyiv, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oleksandra","family":"Kolobylina","sequence":"additional","affiliation":[{"name":"Department of Legal Disciplines, Sumy Branch of Kharkiv National University of Internal Affairs, 24 Miru Street, 40007 Sumy, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oleh","family":"Mieshkov","sequence":"additional","affiliation":[{"name":"Fire and Electrical Research Sector of the Engineering and Technical Research Laboratory, National Scientific Centre \u201cHon. 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