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There are several conclusions obtained from this study. First, The similarity measurement result shows that Fast ICA is the most effective dimensional reduction method. On the other hand, Truncate SVD has yet to deliver satisfactory results. Thus, feature extraction works well on the model with the k-NN algorithm, with the evaluation results on the NCC-2 dataset getting the highest accuracy value of 99.71% and the highest precision of 98.16%. As for testing the NCC-1 model dataset with feature extraction, it obtained the highest score at 99.60% accuracy, 98.72% precision, and 98.42% recall. In testing with the CTU-13 model dataset with feature extraction and the k-NN algorithm also obtained the highest accuracy value of 99.29%, the highest precision of 96.87%, and 95.08% recall. Fourth, The experiment can conclude that k-NN+PCA is the best combination for botnet detection using bidirectional network flow data, with an average performance of 99.53% accuracy, 97.92% precision and 97.15% recall. In contrast, the dimensional reduction technique is unsuitable for tree-based machine learning algorithms like decision tree and Random forest because a single extracted feature forms a model with less complex rules and has an impact on decreasing the detection performance. And The last, Models with Naive Bayes and Logistic Regression algorithms perform well when combined with feature extraction but still require more in-depth analysis to improve detection performance. |
IEEE/ICACT20230225 Slide.24
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