Information Gain applied to reduce model-building time in decision-tree-based intrusion detection system

Due to the large amount of sensitive data generated by websites, it is possible to understand the progress of attacks to their databases. This work proposes an intrusion detection system based on data mining and machine learning techniques to detect and mitigate the damage caused by these attacks. It adopts the Information Gain method of selecting attributes in order to reduce the model-building time without affecting the classification performance. Using the CIC-IDS 2017 dataset, this work shows how different decision tree algorithms (Random Forest and J48 Algorithm) behave even if they receive equal parameters and data. Using Information Gain to select attributes, the proposed system achieves a processing time reduction of up to 90%.