Improve Intrusion Detection for Decision Tree with Stratified Sampling

The present paper aims to improve accuracy of intrusion detection for decision tree algorithm. A number of techniques available for intrusion detection. In this paper we have supervised learning with preprocessing step for intrusion detection. The database is generated i using the stratified sampling techniques and the classification algorithm is applied on the samples. The accuracy of proposed model is compared with existing results in order to verify the validity and accuracy of the proposed model.