arXiv Machine Learning September 24, 2026 By Abdul Khalek Alve, Alif Rahman, Saadman Zaman, Sazzad Hossen Himel, Muhammad Iqbal Hossain

Enhancing Multiclass Malware Classification in Resource-Constrained Environments

Read the original on arXiv Machine Learning →

The paper presents a lightweight machine‑learning approach for multi‑class malware detection on resource‑constrained devices. Using a LightGBM classifier with SMOTE oversampling, SOM‑US undersampling, and Genetic‑Algorithm feature selection, the authors achieve 89.1 % accuracy on four malware families and 76 % on 16 individual malware types. A second Random‑Forest model further improves family classification to 91.2 % and individual classification to 78.7 %.

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