Abstract
Attack types and patterns are constantly evolving which makes frequent detection system updates an urgent need. In contrast, the computation cost of developing machine learning-based detection models such as decision tree classifiers is expensive which can be an obstacle to frequently updating detection models. Tuning classifiers' hyperparameters is a key factor in selecting the best detection model but it significantly increases the computation overhead of the developing procedure. In this research, we have presented a computationally efficient strategy and an algorithm for tuning decision tree classification algorithms' hyperparameters with less budget and time.