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For the experiment, we selected XGBoost model to implement the classification of malicious behaviours. We also designed an automatic optimization process to help the model determine the most suitable algorithm structure in response to different environments. This automatic optimization process has two steps. One is hyperparameter optimization and the other is important features selection.
For the purpose of hyperparameter optimization, the grid search algorithm has been performed. Grid search trains an XGBoost model with the Cartesian product of all hyperparameter sets, and evaluates their performance. The combination of hyperparameters with best accuracy is then chosen for building the XGBoost model.
As for important features selection, we used feature importance calculated by the XGBoost model.
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IEEE/ICACT20220024 Slide.05
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