A Graph-based Online Feature Selection to Improve Detection of New Attacks

Today, intrusion detection systems are used in the networks as one of the essential methods to detect new attacks. Usually, these systems deal with a broad set of data and many features. Therefore, selecting proper features and benefitting from previously learned knowledge is suitable for efficiently detecting new attacks. A new graph-based method for online feature selection is proposed in this article to increase the accuracy in detecting attacks. In the proposed method, irrelevant features are first removed by inputting a limited number of instances. Then, features are clustered based on graph theory to reduce the search space. After the arrival of new instances at each stage, new clusters of features are created that may differ from the clusters created in the previous step. Therefore, to find the appropriate clusters, these two clusters are combined to select some relevant features with minimum redundancy. The evaluation results show that the proposed method has better performance, for instance classification with a lesser run time than similar online feature selection methods. The proposed method is also faster with a suitable accuracy in instances classification compared to some offline methods.