Improve Intrusion Detection Using Decision Tree With Sampling
Intrusion detection system used to discover illegitimate and unnecessary behavior at accessing or manipulating computer systems. The present paper aims to improve accuracy Rate of intrusion detection using decision tree algorithm. Intrusion detection systems aim to identify attacks with a high detection rate and a low Error rate. In this paper, the authors have supervised learning with preprocessing step for intrusion detection. They using the stratified Weighted sampling techniques to generate the samples from original dataset. These sampled applied on the proposed algorithm. The accuracy of proposed model is compared with existing results in order to verify the validity and accuracy of the proposed model.