Research on Intrusion Detection Model Based on Filter Feature Selection Algorithm

In recent decades, various machine learning techniques have been applied to intrusion detection, with Support Vector Machines (SVM) being considered an effective method. However, most research methods based on SVM tend to neglect the importance of data quality, which is crucial for developing high-performance intrusion detection systems. This paper explores the effectiveness of the filter feature selection method. Specifically, the Chi-square test, Pearson Correlation Coefficient, and Maximum Information Coefficient are used as evaluation criteria for feature selection to obtain high-quality training data. Subsequently, the selected data is used to train an SVM classifier to establish an intrusion detection model. Experiments were conducted on two relatively new datasets in the field of intrusion detection, UNSW-NB15 and CICIDS2017. The experimental results indicate that the Pearson Correlation Coefficient and Maximum Information Coefficient methods are more effective and stable.