Euclidean-based Feature Selection for Network Intrusion Detection

Nowadays, data mining has been playing an important role in the various disciplines of sciences and technologies. For computer security, data mining are introduced for helping intrusion detection System (IDS) to detect intruders correctly. However, one of the essential procedures of data mining is feature selection, which is the technique (commonly used in machine learning) for selecting a subset of relevant features for building robust learning models, due to the fact that feature selection can help enhance the efficiency of prediction rate. In the previous researches on feature selection, the criteria and way about how to select the features in the raw data are mostly difficult to implement. Therefore, this paper presents the easy and novel method, for feature selection, which can be used to separate correctly between normal and attack patterns of computer network connections. The goal in this paper is to effectively apply Euclidean Distance for selecting a subset of robust features using smaller storage space and getting higher Intrusion detection performance. During the evaluation phase, three different test data sets are used to evaluate the performance of proposed approach with C5.0 classifier. Experimental results show that the proposed approach based on the Euclidean Distance can improve the performance of a true positive intrusion detection rate especially for detecting known attack patterns.