Model Based Intrusion Detection using Data Mining Techniques with Feature Reduction

As the technology is advancing so are data storing practices. Nowadays stored online which main reason as to why constantly under threat. Therefore there an urgent need of computer se- curity for securing this confidential data, mostly customer personal if got leaked will not only pose threat but also organization liable and preserving that data. These unwanted activities termed intrusions detection these by monitoring analysing system known intrusion detection. IDS created using mining techniques effective way detecting whose implementation discussed ahead in paper. The approach involves building classification model hybrid and, combining both clustering respectively. Classification can detect attacks effectively whereas models unknown or new also. NSL-KDD dataset used training normalalized then its feature reduction done different techniques. best selection technique among all chosen decision table algorithm. comparison results over performance evaluation parameters. show perform better than with improved first preprocessed makes a classifier more effective.