With the monstrous grow in the usage of computers and Internet for information sharing, success of applications running on various platforms causes a serious risk to the security policy. Complex behavior of malwares are also increase, the mechanism to catch malwares also needs improvement. The challenges grow towards the network security due to the introduction of new attacks. This paper emphasize on a hybrid data-mining approach based on ensemble classifier. This preferred approach gives a hybrid classifier which improves the overall detection rate. Preferred approach gives more accuracy and decrease the false positive rate. With this preferred approach the classification accuracy is 99.9894% and the false positive rate is about 0.00. The comparison of preferred approach is made with the single best classifier and it is perceived that the preferred approach gives better results for User to Root (U2R) and Remote to Local (R2L) attack that present in NSL KDD intrusion dataset. This approach gives better results for Root-kit attack.

Intrusion detection system (IDS), false positive rate, NSL-KDD Data set

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