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We choose the TF-IDF algorithm as a feature weighting method for textual analysis and apply three different similarity calculation algorithms, including "Cosine Similarity", "Pearson Correlation Coefficient", and "Euclidean Distance". We can find that the experiment results of all three algorithms are similar. To further assess the effectiveness of our approach in different rule categories, we conduct separate experiments for each rule category.
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Overall, the experiment results show that the F1 score of tactic labeling can reach over 70% and up to 73%, which can significantly help security experts improve the efficiency of tactic labeling. After this stage, a large number of tactic-labeled datasets are produced. Then, we use these accumulated datasets in "Machine Learning-Based." |
IEEE/ICACT20220038 Slide.24
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