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In general, the performance of a botnet detection model also comes from choosing the correct pre-processing technique. Some previous studies give much attention to the pre-processing phase. Like research from Joshi et al, they use a fuzzy logic approach, as a basis for feature engineering and feature selection, with feature participant analysis, before the classification process with an Artificial Neural Network. Feature engineering is also implemented by Putra et al, with a one-hot encoding technique to identify Botnet Attack Communication Pattern Behavior. Other research by Velasco-Mata et al, take two feature selection strategies, Information Reinforcement and Gini Importance, which generate several pre-selected subsets of five, six, and seven features. In other research, Yang et al, detect mobile botnets by implementing a multi-level feature extraction concept to extract info from traffic levels.
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IEEE/ICACT20230225 Slide.07
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