Feature Selection Using Euclidean Distance and Cosine Similarity for Intrusion Detection Model

Nowadays, data mining plays an important role in many sciences, including intrusion detection system (IDS). However, one of the essential steps of data mining is feature selection, because feature selection can help improve the efficiency of prediction rate. The previous researches, selecting features in the raw data, are difficult to implement. This paper proposes feature selection based on Euclidean Distance and Cosine Similarity which ease to implement. The experiment results show that the proposed approach can select a robust feature subset to build models for detecting known and unknown attack patterns of computer network connections. This proposed approach can improve the performance of a true positive intrusion detection rate.