Supervised Learning for Malware Classification
Build and evaluate decision tree and ensemble models to classify malware families from static features.
Supervised learning assumes labeled training data (known malware families and benign software). Analysts extract static features—file size, number of imports, entropy, section count—and train classifiers like decision trees or random forests. Decision trees partition feature space hierarchically, making decisions interpretable (e.g., 'if entropy > 7.5, likely packed'). Random forests combine multiple trees for robustness. These models are fast to train and can classify new samples in milliseconds. However, they require representative training data and fail against novel malware not similar to training examples. Supervised approaches excel for known threats but require periodic retraining as threats evolve.
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