Intelligent intrusion detection based on fuzzy Big Data classification

To cope with the rapid evolution of various attacks and the computer networks' increase, an intelligent intrusion detection system is considered as a promising emerging technique for the security of computer networks. Individual classification approaches have not provided complete protection. Indeed, it has been shown that none of them is efficient enough to provide good detection rates and reduce the false alarms rates. In previous works, a comparative study was conducted between the neuro-fuzzy and the genetic-fuzzy approaches. In this study, a hybrid approach is proposed based on the stacking scheme. This approach offers a solution to combine the two basic classifiers in order to take advantage of each one of them. The experimental results have shown the effectiveness of the proposed approach in terms of maximizing the detection rates and reducing the false alarm rates.

Automated summary

Intelligent intrusion detection system is a promising technique for securing computer networks due to the rapid evolution of attacks and network growth. Individual classification methods have proven to be inefficient in providing good detection rates and reducing false alarm rates. In this study, a hybrid approach based on the stacking scheme is proposed, which combines the strengths of neuro-fuzzy and genetic-fuzzy methods to maximize detection rates and reduce false alarm rates effectively.