Botnets are among the most persistent cyber threats, enabling large-scale attacks such as spam, credential theft, and distributed denial-of-service (DDoS). While deep learning approaches have recently been applied to botnet detection, they are computationally intensive and often lack interpretability. We present a comparative study of lightweight machine learning models including Logistic Regression, Decision Tree, and Random Forest on the CTU-13 dataset, a benchmark for botnet traffic analysis. We extract interpretable flow-based features and evaluate each model on detection accuracy, precision, recall, F1 score, and feature importance. Results demonstrate that lightweight models can achieve competitive detection performance with minimal computational cost, while also offering interpretability critical for forensic investigation. On CTU-13, our Random Forest achieves a PR-AUC of approximately 0.54 and ROC-AUC of 0.97 while training over 90% faster than published CNN baselines. These results demonstrate that lightweight models can match or exceed deep-learning performance under natural class imbalance while maintaining interpretability and low computational cost.

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