A Survey on Different Machine Learning Algorithms and Weak Classifiers Based on KDD and NSL-KDD Datasets

Abstract

Network intrusion detection often finds a difficulty in creating classifiers that could handle unequal distributed attack categories. Generally, attacks such as Remote to Local (R2L) and User to Root (U2R) attacks are very rare attacks and even in KDD dataset, these attacks are only 2% of overall datasets. So,these result in model not able to efficiently learn the characteristics of rare categories and this will result in poor detection rates of rare attack categories like R2L and U2R attacks. We even compared the accuracy of KDD and NSL-KDD datasets using different classifiers in WEKA.

Keywords: KDD, NSL-KDD, WEKA, AdaBoost, KNN, Detection rate, False alarm rate