Intrusion Detection using Classifier Ensemble with Transformed Features

Intrusion detection corresponds to a suite of techniques that are used to identify attacks against computers and network infrastructures. Classifierensembles have been successfully used in many patternrecognition applications. This paper presents a new ensemble-based method for intrusion detection. Thismethod uses feature transformation to create the needed diversity between base classifiers. In other words, first different sets of features are created bymapping the original features into new spaces where the samples are well separated, and then each base classifier is trained on one of these newly created features sets. The proposed method for constructing an ensemble of classifiers is a general method which maybe used in any classification problem. KDD-99 dataset is used to evaluate the proposed method and the results are compared with some recentworks in the literature using the same dataset. The results of comparing the performance of the proposed method with other alternative classification methods are encouraging

کلیدواژه ها:

Intrusion detection ، classifier ensemble ، diversity creation ، feature transformation