Impact Evaluation of Feature Selection Algorithms on Machine Learning-Based Intrusion Detection

Abstract

With the rise of different types of cyber threats, an efficient intrusion detection system (IDS) becomes very crucial for the network security. In this paper, we aim to enhance the performance of the intrusion detection by involving different Feature Selection (FS) algorithms that identify relevant features from high-dimensional datasets, reduce complexity, and improve the model accuracy. We aspire also to prove the importance of the use of large and balanced datasets in enhancing the intrusion detection performances. To do that, we use different recent datasets taken individually or combined with each other. After the preprocessing of the datasets, we apply diverse FS algorithms, and train the machine learning models. The performance evaluation is performed using metrics like accuracy, precision, recall, and F1-score, with an emphasis on analyzing computational efficiency. The obtained results were conclusive and prove the importance of either a balanced dataset or the use of well chosen FS algorithms in improving the intrusion detection.