<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/10012884" mdate="2023-01-04 00:00:00"> <author>Najmeh Abedzadeh and Matthew Jacobs</author> <title>A Survey in Techniques for Imbalanced Intrusion Detection System Datasets</title> <pages>9 - 18</pages> <year>2023</year> <volume>17</volume> <number>1</number> <journal>International Journal of Computer and Systems Engineering</journal> <ee>https://publications.waset.org/pdf/10012884</ee> <url>https://publications.waset.org/vol/193</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>An intrusion detection system (IDS) is a software application that monitors malicious activities and generates alerts if any are detected. However, most network activities in IDS datasets are normal, and the relatively few numbers of attacks make the available data imbalanced. Consequently, cyberattacks can hide inside a large number of normal activities, and machine learning algorithms have difficulty learning and classifying the data correctly. In this paper, a comprehensive literature review is conducted on different types of algorithms for both implementing the IDS and methods in correcting the imbalanced IDS dataset. The most famous algorithms are machine learning (ML), deep learning (DL), synthetic minority oversampling technique (SMOTE), and reinforcement learning (RL). Most of the research use the CSECICIDS2017, CSECICIDS2018, and NSLKDD datasets for evaluating their algorithms.</abstract> <index>Open Science Index 193, 2023</index> </article>