A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering

Many researches have argued that Artificial Neural Networks (ANNs) can improve the performance of intrusion detection systems (IDS) when compared with traditional methods. However for ANN-based IDS, detection precision, especially for low-frequent attacks, and detection stability are still needed to be enhanced. In this paper, we propose a new approach, called FC-ANN, based on ANN and fuzzy clustering, to solve the problem and help IDS achieve higher detection rate, less false positive rate and stronger stability. The general procedure of FC-ANN is as follows: firstly fuzzy clustering technique is used to generate different training subsets. Subsequently, based on different training subsets, different ANN models are trained to formulate different base models. Finally, a meta-learner, fuzzy aggregation module, is employed to aggregate these results. Experimental results on the KDD CUP 1999 dataset show that our proposed new approach, FC-ANN, outperforms BPNN and other well-known methods such as decision tree, the naïve Bayes in terms of detection precision and detection stability.