An efficient architecture for Network Intrusion Detection based on Ensemble Rough Classifiers

The intrusion detection system monitors suspicious activities for the alert system and the network administrator. It becomes more and more important with increasing educational network services. This paper proposes an efficient intrusion detection architecture which named NIDERC (Network Intrusion Detection based on Ensemble Rough Classifiers). The NIDERC contains a new algorithm of attribute reduction which combined Rough Set Theory with Quantum Genetic Algorithm, a method of establishing multiple rough classifications and a process of identifying intrusion data. The experimental results illustrate the effectiveness of proposed architecture.