Authors : Wildote Valério Valente

Volume/Issue : Volume 11 - 2026, Issue 9 - September

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Google Scholar : https://tinyurl.com/2ztkm5jz

DOI : https://doi.org/10.38124/ijisrt/26sep1439

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : Mozambican institutional networks face a growing volume of cyberattacks, with more than 173,000 incidents recorded in 2024, while national incident response capacity is still being consolidated. This paper experimentally evaluates machine learning techniques for intrusion detection and for its anticipation, defined as classifying a connection as malicious using only the information available at its start. The public UNSW-NB15 dataset (175,341 training and 82,332 test records, nine attack categories) was used to compare four algorithms: Logistic Regression, Random Forest, XGBoost and a Multilayer Perceptron.

Keywords : Cybersecurity, Intrusion Detection, Machine Learning, XGBoost, UNSW-NB15, Mozambique.

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