Every machine learning project should follow specific steps to achieve its goal. The first step is data processing—during this step we need to extract the meaningful features from the raw data. This step is crucial because good feature engineering is needed to build a good machine learning model. After processing the data, we have to train and choose the best predictive model for our situation. Finally, after training the model, evaluation is an important process where we check the accuracy and the performance of the trained model to predict new data.
Many IDS, based on machine learning, have begun to surface. They can create great solutions for detecting unknown threats, while network security engineers ...
