A New Feature Selection Method for Intrusion Detection System Dataset – TSDR method

In recent years, due to the increased frequency of cyber-attacks, the negative impacts of cyber-attacks on society have increased. Therefore, the research on cyber-security and prevention of cyber-attacks, including intrusion detection as an effective means of defense against cyber-attacks, is warranted. Both in the research and in the development of the systems for intrusion detection, the machine learning and deep learning methods are widely utilized, and the NSL-KDD dataset is frequently used in algorithm research and verification. In this paper, we propose a new two-stage dimensionality reduction (TSDR) feature selection method and verified by NSL-KDD dataset. The method reduces the dimensionality of the dataset and significantly improves the calculation efficiency. The KNN algorithm is used to verify that the new feature selection method improves the calculation efficiency. The accuracy rate is only slightly reduced when compared to the full feature calculation.