文章基本信息

标题：A Survey on Different Machine Learning Algorithms and Weak Classifiers Based on KDD and NSL-KDD Datasets
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作者：Rama Devi Ravipati ; Munther Abualkibash
期刊名称：International Journal of Artificial Intelligence & Applications (IJAIA)
印刷版ISSN：0976-2191
电子版ISSN：0975-900X
出版年度：2019
卷号：10
期号：3
页码：1-12
DOI：10.5121/ijaia.2019.10301
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：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..
关键词：KDD; NSL;KDD; WEKA; AdaBoost; KNN; Detection rate; False alarm rate