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The central aim is to explore machine learning algorithms designed for dynamic malware detection.

January 22, 2026Open Access

A Review of Machine Learning Algorithms for Malware Detection

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The central aim is to explore machine learning algorithms designed for dynamic malware detection.
Employs automated behaviour-based detection in a controlled environment.
Malware samples are monitored and converted into sparse vector representations.
Classifiers used include kNN, DT, RF, AdaBoost, SGD, Extra Trees, and Gaussian NB.
RF, SGD, Extra Trees, and Gaussian NB achieved 100% accuracy on the test set.
All classifiers demonstrated perfect precision, recall, and f1-scores of 1.00.

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This review examines machine learning techniques for detecting malware, implying they enhance security measures.

Singh et al. (2026) studied this question.

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