| Title: | Machine learning-based detection of complex cyberattacks |
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| Authors: | ID Hölbl, Marko (Author)
ID Rotovnik, Maja (Author) |
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| Files: |  s10586-026-06367-4.pdf (2,27 MB)
MD5: 3A35B635C8C2C3D34FDD7726A1993F31
 https://link.springer.com/article/10.1007/s10586-026-06367-4?utm_source=rct_congratemailt&utm_medium=email&utm_campaign=oa_20260723&utm_content=10.1007/s10586-026-06367-4
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | With the increasing complexity of modern cyberattacks, such as advanced persistent threats, reconnaissance, and stegan ography, traditional rule-based and signature-based detection methods are becoming less effective. Machine learning (ML) provides advanced capabilities for identifying sophisticated and stealthy attacks by efficiently processing large volumes of data and uncovering hidden patterns. This paper presents a systematic review of existing approaches to complex cyberat tack detection based on machine learning techniques, encompassing an analysis of 68 research articles. The review evalu ates the performance of individual algorithms compared to ensemble approaches, examines commonly used ML methods, and analyzes datasets used in experimental studies. The results show that ensemble models generally outperform indi vidual classifiers, with detection accuracy improvements ranging from 0.4 % to 28.52 %. Machine learning methods such as XGBoost, Random Forest, and LightGBM are identified as particularly effective across various attack types. Supervised learning remains dominant, though interest in unsupervised and semi-supervised methods is increasing to address novel threats. Frequently used datasets include NSL-KDD, UNSW-NB15, and newer APT-focused datasets such as DAPT2020 and SCVIC-APT-2021. The findings confirm the strong potential of ML for adaptive and proactive cybersecurity systems. |
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| Keywords: | complex cyberattacks, machine learning, ensemble methods, advanced persistent threats, reconnaissance, steganography |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 26.02.2026 |
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| Article acceptance date: | 03.07.2026 |
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| Publication date: | 23.07.2026 |
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| Publisher: | Springer Nature |
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| Year of publishing: | 2026 |
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| Number of pages: | 23 str. |
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| Numbering: | Vol. 29, [article no.] 551 |
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| PID: | 20.500.12556/DKUM-99001  |
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| UDC: | 004.85:004.056.5 |
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| ISSN on article: | 1573-7543 |
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| COBISS.SI-ID: | 285910275 |
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| DOI: | 10.1007/s10586-026-06367-4 |
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| Publication date in DKUM: | 24.07.2026 |
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| Views: | 235 |
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| Downloads: | 14 |
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| Metadata: |    |
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| Categories: | Misc.
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