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 classi?cation technique for intrusion detection might involve some di?culties and limitations such as high complexity, instability, and low detection precision for less frequent attacks. Ensemble classi?ers can address these issues as they combine di?erent classi?ers 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 classi?cation techniques, particularly when the classes of attacks are small.