DATA MINING FOR THE EARLY DETECTION OF CYBERATTACKS ON ENTERPRISE NETWORKS.
The early detection of cyberattacks is critical to protecting enterprise networks. This paper proposes a method that uses data mining and machine learning techniques to identify harmful traffic on computer networks. The UNSW-NB15 dataset was used as a reference for testing this method. The CRISP-DM...
| Publicado en: | Scientific Culture Vol. 12; no. 1, Part 1; pp. 2979 - 2992 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
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University of the Aegean
2026
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=192213748&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 192213748 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 24080071 I6HU jtl: Scientific Culture issn: 24080071 maglogo: N pubinfo: dt: 2026 vid: 12 iid: 1, Part 1 pid: 47715 pub: University of the Aegean artinfo: ui: 192213748 10.5281/zenodo.121126217 ppf: 2979 ppct: 13 formats: tig: atl: DATA MINING FOR THE EARLY DETECTION OF CYBERATTACKS ON ENTERPRISE NETWORKS. aug: au: Morales, Brandon Panduro, Enmanuel Luque, Dikxon Andrade, Teodoro Chiri, Carlos affil: Universidad Nacional Mayor de San Marcos (UNMSM), Perú. Universidad San Ignacio de Loyola (USIL), Perú. su: Data mining Intrusion detection systems (Computer security) Random forest algorithms Information technology security Enterprise networks (Telecommunications) Machine learning sug: subj: Data mining Intrusion detection systems (Computer security) Random forest algorithms Information technology security Enterprise networks (Telecommunications) Machine learning keyword: artificial intelligence computer vision data mining Deep Learning intrusion detection ab: The early detection of cyberattacks is critical to protecting enterprise networks. This paper proposes a method that uses data mining and machine learning techniques to identify harmful traffic on computer networks. The UNSW-NB15 dataset was used as a reference for testing this method. The CRISP-DM methodology was applied, ranging from understanding the data to evaluating the model. Univariate and bivariate exploratory analyses were carried out to select relevant characteristics for the study. Joint learning algorithms, such as Random Forest, Extra Trees, AdaBoost, and XGBoost, were used. Results show that models using the bagging method, particularly Random Forest, perform much better than boosting-based models in metrics such as accuracy (0.98), recall (0.99), and F1-score (0.98) in the attack category. It is concluded that collective learning approaches are an effective, understandable, and low-computational-cost alternative for automatically detecting intrusions in corporate networks. This study highlights the feasibility of integrating robust data analytics approaches into advanced cybersecurity systems. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2026 holdings: @attributes: islocal: N |
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