文章基本信息

标题：Intrusion Detection System Classification Using Different Machine Learning Algorithms on KDD-99 and NSL-KDD Datasets - A Review Paper
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作者：Ravipati Rama Devi ; Munther Abualkibash
期刊名称：International Journal of Computer Science & Information Technology (IJCSIT)
印刷版ISSN：0975-4660
电子版ISSN：0975-3826
出版年度：2019
卷号：11
期号：3
页码：1-16
DOI：10.5121/ijcsit.2019.11306
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because itâs unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate..
关键词：Intrusion Detection System; KDD;99 cup; NSL;KDD; Machine learning algorithms