AN ANALYSIS ON NSL-KDD DATASET USING MACHINE LEARNING TECHNIQUES FOR INTRUSION DETECTION

Abstract

With the growth of the usage of computers over the network, security vulnerabilities on all the computer systems seem very difficult and expensive. The Intrusion Detection System (IDS) generates huge numbers of false alerts. Therefore, it is necessary to assist in categorizing the degree of threat by using data mining techniques. We have used the NSL-KDD dataset for this research study. The response time was found to be high when the complexity of the dataset is high. Therefore, we have utilized Infogain feature selection algorithms. Four machine learning classification algorithms such as Sequential Minimal Optimization, Nave Bayes, J48, and Random Forest are utilized for this study. The Random Forest scores the best accuracy at 99.9%. However, J48 was chosen with an accuracy score of 99.8% with a minimum response time.