Effective Intrusion Detection with a Neural Network Ensemble Using Fuzzy Clustering and Stacking Combination Method

Data mining techniques are widely used for intrusion detection since they have the capability of automation and improving the performance. However, using a single classification technique for intrusion detection might involve some difficulties and limitations such as high complexity, instability, and low detection precision for less frequent attacks. Ensemble classifiers can address these issues as they combine different classifiers and obtain better results for predictions. In this paper, a novel ensemble method with neural networks is proposed for intrusion detection based on fuzzy clustering and stacking combination method. We use fuzzy clustering in order to divide the dataset into more homogeneous portions. The stacking combination method is used to aggregate the predictions of the base models and reduce their errors in order to enhance detection accuracy. The experimental results on NSL-KDD dataset demonstrate that the performance of our proposed ensemble method is higher compared to other well-known classification techniques, particularly when the classes of attacks are small.